Where Place Meets Policy: Spatial Histories,Policy Logics,and the Making of Disproportionality

Abstract

Racial disproportionality in special education persists as a spatialized injustice shaped by local histories and geographies. Using a critical-spatial lens, we conducted an explanatory sequential mixed-methods study to examine how context mediates both disproportionality citations and district responses. Quantitative analyses of California data showed that large-city districts serving high proportions of Black, Indigenous, and Latine students were cited more frequently than similarly composed small-town and rural districts. Guided by those findings, our qualitative analysis of district policy documents revealed how racial composition shaped local understandings of root causes and solutions. We integrated qualitative and quantitative results in the culminating analysis. Findings underscored how sociohistorical and geographic forces structured policy implementation, pointing to the need for equity-driven reforms that center place-based organizational change.

Introduction

Decades of research and policy work have failed to yield consensus on how to address racial disparities in special education, leaving the phenomenon well documented yet unresolved (Albrecht et al., 2012). Although recent scholarship has foregrounded the localized ways that geographic, sociocultural, and historical planes shape disproportionality (e.g., Bal et al., 2014; Mawene et al., 2024; Tefera et al., 2023), contextual influences on districts’ planned solutions remain underresearched. Local context influences educators’ understandings of students (Turner, 2015), including how they view variation in learning preferences, assets, and lived experiences of injustice (Green et al., 2023). However, approaches that conceptualize disproportionality as a monolithic, structural reproduction of societal hierarchies (Sullivan & Artiles, 2011) or, conversely, as rooted in interpersonal bias (Harry et al., 2005; Owens, 2023) have failed to leverage local understandings (Ray, 2019). Research must examine space's influence on local actors’ beliefs and actions, including how sociohistorical and geographic characteristics (re)produce injustices in mutually constitutive ways (Soja, 2013). Centering local context in transformative efforts holds the potential to capture novel perspectives on inequities and leverage community assets in generating sustainable solutions (Reupert et al., 2022).

Federal special education law (Individuals with Disabilities Education Improvement Act [IDEA], 2004) requires states to monitor and cite districts with disproportionate racial representation both in special education overall and in specific disability categories. When cited for 3 consecutive years, districts must create a plan to rectify the disproportionality, despite limited evidence that this requirement leads to change. Thus far, studies examining disproportionality policy implementation have either qualitatively focused on a single district (e.g., Tefera & Fischman, 2020; Voulgarides et al., 2021) or quantitatively analyzed regional data from the northeastern United States (e.g., Aylward et al., 2026; Aylward et al., 2021). Given the latitude that states have in interpreting the disproportionality requirements of the Individuals with Disabilities Education Improvement Act [IDEA], research in other regions and with methods that capture influences across space and time may substantiate how local context informs policy use and practice (Albrecht et al., 2012; Elbaum, 2014). In this study, we addressed this gap through an analysis of California's disproportionality data.

We employed a three-phase explanatory sequential mixed-methods design (Fetters, 2019). Phase 1 comprised a quantitative analysis of district citations, using a dataset from the California Department of Education (CDE) from 2010–11 to 2018–19. ¹ In addition to featuring the country's largest and most diverse student population, California has the greatest proportion of students who speak a language other than English at home (California Department of Education [CDE], 2019), offering a rich context for research. Quantitative results depicted racial disproportionality citation patterns over time, which informed the qualitative analysis of how districts interpreted and responded to the citation in their required policy documents in Phase 2. In Phase 3, we integrated findings to generate meta-inferences that transcended those mono-methodologic results (Guetterman et al., 2021). With an overarching focus on the intersections of policy and geography in relation to special education racial disproportionality, we answered the following research questions: (a) Across California, how were districts’ locale, location, and composition associated with their likelihood of receiving a citation? (b) How did those districts rationalize root causes and conceptualize solutions in their citation-response plans? and (c) How did historical-temporal elements of geography relate to districts’ understandings and proposed solutions?

We defined the study's spatial components as follows: Locale was a district's geographic classification from the National Center for Education Statistics (NCES, n.d.), which specifies four main types: city, suburban, town, and rural; each type is divided into three subtypes based on population size and proximity to populated areas: large, midsize, or small. Location was a more geographically specific region, based on California's Special Education Local Plan Areas (CDE, 2024); these organize districts and county offices into consortiums based on CDE-defined geographic regions (see Figure 1, Map 1). Finally, composition referred to districts’ demographic makeup, specifically the percentage of Black, Indigenous, and People of Color (BIPOC) and the percentage of emerging multilingual learners (EMLs). We further describe these spatial components in relation to our conceptual framework, which articulates critical spatial theory with a pluralistic theory of causation from the mixed-methods paradigm (Johnson et al., 2019).

Figure 1.

Defining location using California's Special Education Local Plan Area (SELPA) Bivariate Choropleth Maps indicates the regional distribution of citations by composition. Map 1: California's geographic locations. Map 2: Regional distribution of citations by percentage Black, Indigenous, and People of Color. Map 3: Regional distribution of citations by percentage emerging multilingual learners.

Conceptual Framework

Public education exemplifies a social field in which (in)justice is produced, reproduced, and contested through the organization of space (Soja, 2013). In Soja's sociospatial dialectic, geography is not a neutral backdrop. Rather, it actively structures landscapes of advantage by concentrating material resources, political power, and cultural capital in some places while withholding them elsewhere. Attendance-zone boundaries, fiscal policies, and even the microgeographies of within-school tracking function as instruments that unevenly distribute educational opportunity (Hogrebe & Tate, 2017). A student's location influences how they are sorted into schools and the educational programming offered within those schools, which exacerbate disparate access to resources (Turner, 2020).

Research has illustrated how spatial injustices intersect with race and disability to shape Black and Latine students’ likelihood of being labeled with disabilities (Fish, 2019; Stiefel et al., 2024). For example, research has found that racialized and minoritized students who attend schools with higher proportions of White peers face elevated odds of being classified as “learning disabled” (Shifrer, 2018, p. 384), whereas Black and Latine students were identified at lower rates in schools with proportionally fewer White students (Elder et al., 2021). Because school composition reflects historically constructed segregation (Clotfelter, 1998), these patterns suggest structural processes through which spatial context arbitrates educational outcomes (Cruz et al., 2025). Leveraging Soja's framework, we contend that districts’ geographic characteristics—location, locale, and composition—act as spatial filters that shape special education identification patterns, exposing not random variation but the spatial production of inequity. Bal’s (2017)“social-historical-spatial conceptualization” (p. 2) of racial disproportionality underscores this view, describing how systemic inequities are produced through recursive temporal and spatial forces.

Indeed, disproportionality is not simply a technical classification problem. Rather, it emerges from and perpetuates systemic contradictions in mutually reinforcing ways—what Bal (2017) called the “system of disability” (p. 10). In addition to identification practices, this system functions through an architecture of governance, sorting, and exclusion that frames students as deficient or deviant. Thus, spatiality is more than place; it constitutes how people experience, legitimize, and reproduce educational inequities. Yet, policy-driven responses to racial disproportionality conceptualize the phenomenon as a technical one, obscuring spatial and historical dimensions that manifest locally, failing to realize equitable access to high-quality education (Turner, 2015). In many districts, mandated correction plans adopt race-evasive, deficit-oriented approaches, often selected from a standard set of solutions that emphasize technical compliance over transformative change (Tefera & Fischman, 2020; Voulgarides et al., 2021). Bal's critique underscored how such reforms fail to confront the racialized and spatialized logics embedded in educational systems and, instead, reinscribe inequity through standardization and performative compliance.

We drew on Soja’s (2013) theorization of spatialities—lived, perceived, and conceived dimensions of space—and Bal’s (2017) call for sociocultural, process-oriented analyses that locate disproportionality within the everyday contradictions of educational systems. We interrogated assumptions of spatial neutrality and generalizability by situating identification patterns within the social-societal and spatial-geographic dynamics that shape institutions (Lefebvre, 1974; Soja, 2013). In doing so, our framework advances researchers’ calls to foreground local context in disproportionality research (Bal et al., 2014; Joyce & Cartwright, 2020), emphasizing how districts’ spatial characteristics reflect political and ideologic contours (Freidus, 2020; Johnson, 2012). We examined how local actors constructed and enacted meanings of race and ability within the IDEA policy framework, recognizing how they shape not only identification rates but also educational opportunity, in a broad sense (Tefera & Fischman, 2020; Turner, 2020). To operationalize spatiality, in our analysis we disaggregated district-level data by location (i.e., California's geographic regions), locale type (i.e., city, suburban, rural, and town), and composition (Green et al., 2023). In district policy documents, we attended to the local perspectives—of parents, educators, and community members, who shape students’ educational experiences (Reupert et al., 2022).

In addition, we adopted a pluralistic theory of causation from the mixed-methods paradigm (Johnson et al., 2019). This methodologic stance eschews linear, reductionist explanations, instead foregrounding the entanglement of structural, spatial, and sociocultural forces that shape inequities. Rather than aiming to generalize patterns of disproportionality across districts, our mixed-methods analysis surfaced nuanced, locally embedded dynamics. Through integrating qualitative and quantitative findings, we foregrounded the intersecting influences of race, space, and policy enactment (Vélez & Solórzano, 2018), drawing on both macro-level understandings and localized perspectives to generate “bird’s-eye and on-the-ground perspectives” (Lubienski & Lee, 2017, p. 95). We used spatially attuned visualizations and analyses to trace how spatial histories and built environments—including urban displacement, disinvestment, and local industries (Butler & Sinclair, 2020)—shaped districts’ disproportionality patterns.

Current Policy Landscape

The 2004 congressional reauthorization of IDEA mandated that state education agencies monitor and report racial/ethnic representation within districts. The federal law defines racial-equity metrics—State Performance Plan Indicators—that districts must report to their states. Indicators 4, 9, and 10 require districts to report racial/ethnic data on suspensions and expulsions of students in special education (i.e., Indicator 4), overall special education classification (i.e., Indicator 9), and representation across IDEA's disability categories (i.e., Indicator 10; Office of Special Education Programs, 2017). States use these annual reports to identify and cite districts with disproportionate representation of any racial/ethnic group.

Federal law requires cited districts to conduct an audit of policies, practices, and procedures related to the disproportionality. When a district is cited for 3 consecutive years, the state classifies it as significantly disproportionate, triggering requirements to conduct a root cause analysis (O’Hara & Bollmer, 2021) and develop a Comprehensive Coordinated Early Intervening Services (CCEIS) plan, including reallocating 15% of IDEA Part B funds to implement services specified in the plan to address the disproportionality (IDEA, 2016, 34 CFR, § 300.646). For example, a district cited for disproportionate suspensions of Black students in special education (i.e., Indicator 4) might review its discipline policies and develop a corrective plan that revises those policies and related practices, reallocating funds to support that work. Federal law encourages family engagement throughout this process, and many states—including California—interpret this guidance to require that family input be included in this planning process. The state's guidance emphasizes engaging caregivers and community actors to ensure that the root cause analysis and plan reflect those local perspectives.

Yet, researchers have critiqued this policy remedy as compliance focused (Voulgarides et al., 2021), inconsistently applied (Albrecht et al., 2012), and rooted in race-evasive logics that fail to address structural and systemic inequities (Tefera & Fischman, 2020; Voulgarides et al., 2025). Although IDEA mandates attention to disproportionality, states retain broad discretion in implementation (Albrecht et al., 2012), and districts have considerable leeway in interpreting and enacting state-level guidance. Interactions across federal policy, state discretion, and district enactment reflect the layered dynamics of educational governance (Fuller, 2020), and the mechanisms through which these forces converge remain underexplored. Beyond a mere numerical imbalance, we viewed disproportionality as a spatially embedded manifestation of systemic inequities. By centering local communities within a critical-spatial framework, we employed a mixed-methods design to illuminate how structural forces and community agency intersect to shape policy enactment and equity reform on the ground.

Methods

We employed an explanatory sequential mixed-methods design (e.g., Fetters, 2019) to examine disproportionality as a spatially embedded phenomenon (see Figure 2). In (quantitative) Phase 1, we analyzed statewide data to identify longitudinal trends in disproportionality citations across locations and locales, given district composition. These findings informed (qualitative) Phase 2, wherein we analyzed policy documents from a purposefully selected subset of cited districts to explore how local actors interpreted and responded to disproportionality. In Phase 3, we integrated the two strands of findings to generate meta-inferences—insights that synthesized quantitative patterns and qualitative themes (Creamer, 2018)—focused on the historical-temporal dimensions of districts’ geography in relation to their policy interpretations. In alignment with our conceptual framework, this design positioned local actors as spatial agents (Yoon & Lubienski, 2018) whose policy interpretations both reflected and reshaped the multilayered geographies they inhabited.

Figure 2.

Procedural diagram: Explanatory sequential mixed-methods design.

Data

Quantitative Strand

We constructed a district-year panel dataset spanning 2010–11 to 2018–19 using two sources. The first was a restricted dataset of all California public, noncharter districts ² (N = 1,028), which included binary indicators for citations on Indicators 4, 9, and 10. Using county-district-school identifiers, we merged this with publicly available data from California's Longitudinal Pupil Achievement Data System ³ (CDE, 2019), incorporating district racial and linguistic composition as the percentage of students identified as BIPOC and EML. The final dataset included 6,043 total observations across 768 unique districts, averaging 755 districts per year. NCES locale classifications were city (19.8%), suburban (36.9%), town (19.4%), and rural (23.9%). On average, districts enrolled 62.4% BIPOC (overall SD = 24.4, between-year SD = 24.2, within-year SD = 3.6, minimum = 4.7, and maximum = 99.7) and 17.4% EMLs (overall SD = 14.0, between-year SD = 13.5, within-year SD = 4.0, minimum = 0, and maximum = 78.94). By capturing both spatial positioning (i.e., locale and location) and composition, the dataset reflected California's geographic and demographic diversity, enabling our examination of how spatial and racial configurations intersected with patterns of disproportionality.

Qualitative Strand

When a district is classified as significantly disproportionate, it must create a team tasked with developing and implementing a CCEIS plan, including the collection of input from internal and external partners to identify contributing conditions. Districts often draw on survey and interview data from the school community, including teachers, school psychologists, and families. In this study, we used the quantitative findings to purposefully select a subsample of 19 districts from the pool of those cited for significant disproportionality (N = 203), ensuring variation across citation patterns, locales, and locations (e.g., suburban districts cited for overrepresentation of Black students in discipline [Indicator 4] situated in SELPA area 4 and town districts cited for overrepresentation of White students in Autism [Indicator 10] situated in SELPA area 5). For each district, we collected publicly available CCEIS plans and related documents (e.g., training materials and Local Control and Accountability Plans). We generated a 27-document dataset, which we used to analyze how districts across geographic and demographic contexts described and responded to disproportionality mandates. In line with our spatial framework, we treated these texts as artifacts of place-based sensemaking, reflecting how local communities interpreted the policy. We analyzed documents in MAXQDA (version 20.4.2; VERBI Software GmbH, Berlin, Germany).

Analytic Plan

Quantitative Strand: Spatial Patterns of State Policy Citations

Multilevel Analysis

To answer our first research question—how geographic factors were associated with districts’ likelihood of receiving a disproportionality citation—we conducted logistic regressions using the 8-year district-year panel dataset. The dependent variable was whether a district received a citation under Indicator 4, 9, and/or 10 in a given year. Key predictors were district locale type (i.e., city, suburban, town, with rural serving as the referent) and demographic composition, specifically the percentages of students identifying as BIPOC and EMLs. Year fixed effects accounted for unobserved policy changes over time, and we standardized time-varying composition variables (i.e., percentage BIPOC and EML) to z scores for interpretability. We clustered standard errors by year.

Given the longitudinal data's dependent structure (Rabe-Hesketh & Skrondal, 2022), we modeled both time-invariant (i.e., locale) and time-varying (i.e., composition) covariates. The following equation represented the final model:

$$\mathrm{logit}[\mathrm{Pr}(y_{\mathrm{ij}}=1|x_{\mathrm{ij}})]={\beta }_0+{\beta }_1{x}_{\mathrm{ij}}+{\beta }_2{x}_j+({\beta }_1{x}_{\mathrm{ij}}\times {\beta }_2{x}_j)+{\mathrm{\delta }}_j$$

where y is a binary indicator of whether a district $i$ experienced a citation in year $j$ , ${\beta }_1{x}_{\mathrm{ij}}$ is a vector of time-invariant district-level variables (e.g., locale), and ${\beta }_2{x}_j$ represents time-varying characteristics (e.g., percentage BIPOC), with ${\mathrm{\delta }}_j$ as a fixed effect for year. In the final model, we included interaction terms between composition (i.e., percentage BIPOC and percentage EML) and locale to assess whether composition effects varied across geographic contexts. The model estimated the log odds of citation as a function of locale, composition, and their interactions. Figure 3 presents an odds-ratio plot summarizing the final model, and Table 1 outlines model specifications with each covariate addition. We then engaged in the spatial analysis, after which we applied quantitative findings to inform the qualitative phase.

Figure 3.

Predicted citation patterns: Odds ratio plot by locale, composition, and interaction terms.

Table 1

Logistic Regression Models: Odds Ratios Predicting District Disproportionality Citation

Variable	Model 1	Model 2	Model 3	Model 4	Model 5
Fixed
Intercept	0.11 (0.06)***	0.11 (0.08)**	0.12 (0.08)**	0.11 (0.08)**	0.11 (0.08)***
Locale type
City	5.78 (0.72)***	4.42 (0.59)***	4.37 (0.58)***	4.36 (0.63)***	4.27 (0.62)***
Suburb	3.72 (0.42)***	3.00 (0.35)***	2.76 (0.32)***	2.75 (0.34)***	2.79 (0.35)***
Town	1.87 (0.24)***	1.68 (0.22)***	1.75 (0.23)***	1.77 (0.25)***	1.78 (0.25)***
Composition
% BIPOC	—	1.33 (0.06)***	1.94 (0.12)***	1.70 (0.18)***	1.55 (0.20)**
% EML	—	—	0.60 (0.04)***	0.61 (0.04)***	0.70 (0.09)**
% BIPOC × locale
City	—	—	—	1.28 (0.18)	1.63 (0.32)*
Suburb	—	—	—	1.36 (0.16)**	1.43 (0.23)*
Town	—	—	—	0.79 (0.11)	0.88 (0.18)
% EML × locale
City	—	—	—	—	0.73 (0.13)
Suburb	—	—	—	—	0.95 (0.15)
Town	—	—	—	—	0.87 (0.17)
Year random intercept
Sigma_u	1.89 (0.47)	1.90 (0.48)	1.92 (0.48)	1.93 (0.49)	1.93 (0.48)
Rho	0.52 (0.13)	0.52 (0.13)	0.52 (0.12)	0.53 (0.13)	0.53 (0.13)
AIC	4,462.28	4,418.66	4,347.78	4,329.34	4,231.21
BIC	4,495.81	4,458.90	4,394.92	4,396.40	4,318.40

Note. Standard errors are in parentheses. BIPOC = Black, Indigenous, and People of Color; EML = EML = emerging multilingual learner; AIC = Akaike information criterion; BIC = Bayesian information criterion.

*p < .10; **p < .05; ***p < .01.

Spatial Analysis

To investigate variation by location, we constructed a series of choropleth maps (Slocum & Egbert, 1993) using the U.S. Census Bureau's 2016 cartographic boundary file (U.S. Census Bureau, 2019). Maps depicted total citation counts across California's 11 geographic locations (see Figure 1, Map 1). Descriptively, these regions delineate, for example, coastal locations (e.g., areas 1, 5, and 8), urban centers (e.g., areas 3, 4, and 11), desert areas (e.g., area 10), and agricultural centers (e.g., area 7). To visualize the racial and linguistic distribution of students across these 11 locations, we created two choropleth maps showing average district-level percentages of BIPOC (see Figure 1, Map 2) and EMLs (see Figure 1, Map 3). On Maps 2 and 3, darker shades of red represent higher citation counts across years, and darker shades of blue represent higher average percentages of BIPOC and EMLs. These spatial visualizations indicated how composition and location intersected in the production of disproportionality patterns across the state.

Together the quantitative findings foregrounded systemic spatial inequities and highlighted concentrated patterns of disproportionality. In Phase 2, we developed ground-level pictures of these locations, exploring local perspectives in district plans and focusing on interpretations of and engagement with these spatially embedded inequities.

Qualitative Strand: Locally Situated Sensemaking

Using a multiple-case-study approach (Stake, 2013; Yin, 2014) bounded in conceptually informed criteria (Creswell, 2013), we analyzed CCEIS plans and related documents from 19 purposefully selected districts (Hennink & Kaiser, 2022). Documents depicted how local actors framed disproportionality and proposed reforms within their geographic and organizational contexts. We conducted an iterative two-stage systematic analysis (Cardno, 2018). First, we performed eclectic coding (Saldaña, 2021) with a combination of a priori and emerging codes. A priori codes focused on districts’ explanations and proposed remedies for disproportionality and salient characteristics within and across geographies, including (a) district location (e.g., agricultural, urban center), (b) locale type and size (e.g., large city, small rural), (c) citation type (e.g., Latine students in a Specific Learning Disability [SLD] category), (d) posited root causes (e.g., eligibility processes), (e) proposed solutions (e.g., early intervention), and (f) local perceptions. After an initial round of applying these codes, we refined the codebook and then recoded all documents to ensure consistency.

In the second stage, we used pattern coding to generate broader themes across cases (Saldaña, 2021), distilling how districts constructed and justified their planned responses to disproportionality. To ensure accuracy and consistency, the research team met regularly to compare, refine the codebook, sharpen definitions, and prevent drift. Throughout the analysis, we treated documents as situated texts reflecting how local actors interpreted and enacted policy within spatial, political, and historical conditions.

Integrative Analysis: Weaving Structures and Local Sensemaking into a Causal Mosaic

Mixed-methods research designs hinge on the rigorous integration of qualitative and quantitative findings, and integration occurred at two key points of our sequential design (Creamer, 2018): first, when we used quantitative findings to inform case selections in the qualitative phase and, second, when we merged findings in Phase 3 to examine how historical-temporal elements of geography intersected with local sensemaking (see Figure 2). Through this systematic integration of findings across strands, we generated meta-inferences (Guetterman et al., 2021).

In this phase, we used joint display analysis—a mixed-methods technique that juxtaposes quantitative and qualitative findings at comparable levels of aggregation (Haynes-Brown & Fetters, 2021)—to integrate district-level quantitative patterns with qualitative representations of local actors’ framings of disproportionality. Through recursive rounds of constructing, analyzing, critiquing, and reconstructing district-specific joint displays, we considered how local interpretations reconstituted structural conditions (Kolluri & Tichavakunda, 2023; Merolla & Jackson, 2019). Consistent with our conceptual framework, this integrative process allowed us to interrogate not only where disproportionality occurred but also how people in those spaces understood, framed, and contested it on the ground—as reflected in policy documents. Throughout the integrated analysis, we used location to interpret historically situated patterns of composition, citation, and sensemaking; we do not claim statistical causality but rather theorize how these features co-constitute policy enactment in place. In the “Results” section, we describe how assessing coherence across each strand's findings functioned as analytic building blocks for the joint display analysis and meta-inference generation.

Results

Leveraging our conceptual framework (Vélez & Solórzano, 2018), we examined how geography—locale, location, and demographic composition—shaped likelihood of citation and local understandings of disproportionality. Although locale and composition can appear to explain spatial patterns on their own, we used location to identify the regional political-economic and sociohistorical conditions (e.g., housing displacement, labor markets, and local industry) through which composition is produced and through which districts interpret and respond to citations. We found that districts experienced disproportionality citations not as a neutral indicator but as a spatially situated event, shaped by power structures and interpreted through local systems of meaning.

Quantitative Findings: Shifting Denominators Across Locales

California calculates disproportionality using a relative risk ratio that compares the likelihood of a specific outcome (e.g., special education eligibility) for each racial/ethnic group against all others. For example, the state calculates Black students’ risk of placement in special education using the following formula:

$$\mathrm{Risk}\mathrm{ratio}=\frac{\mathrm{number}\mathrm{of}\mathrm{Black}\mathrm{students}\mathrm{placed}/\mathrm{number}\mathrm{of}\mathrm{Black}\mathrm{students}\mathrm{enrolled}}{\mathrm{number}\mathrm{of}\mathrm{all}\mathrm{other}\mathrm{students}\mathrm{placed}/\mathrm{number}\mathrm{of}\mathrm{all}\mathrm{other}\mathrm{students}\mathrm{enrolled}}$$

The formula's denominator implicates district composition in citation likelihood (Aylward et al., 2026).

In Phase 1, we asked how districts’ locale, location, and composition related to their likelihood of receiving a citation. Logistic regression results indicated that districts in city locales were most likely to be cited, followed by suburban, town, and rural locales. In the final model (Table 1), all net controls considered, city districts had more than four times the odds of citation compared with rural districts, with an odds ratio (OR) of 4.27 (SE = 0.62, z = 9.94, p < .001), and suburban districts had nearly three times the odds (OR = 2.79, SE = 0.35, z = 8.17, p < .001), controlling for district composition (Figure 3). Each standard deviation increase in the percentage of BIPOC enrolled was associated with roughly a 50% increase in the odds of citation, whereas each standard deviation increase in EML enrollment was associated with a 30% decrease in the odds of citation. We investigated these patterns using two locale-by-composition interaction terms: percentage BIPOC × locale and percentage EML × locale. In both city and suburban districts, greater BIPOC enrollment was associated with higher odds of citation, whereas the opposite pattern emerged in town districts. Finally, higher EML enrollment was associated with a slightly lower odds of citation in city districts. ⁴

Spatial visualizations further illustrated these dynamics in relation to location. Figure 1 (Maps 2 and 3) shows citation density and racial-linguistic composition across California's geographic regions. Darker red shades represent higher disproportionality citation counts; darker blues represent higher proportions of BIPOC (Map 2) and EMLs (Map 3). Purple therefore represents locations with both high citation counts and high shares of BIPOC or EMLs. The dark purple shades in large, urban-center locations, housing districts such as Sacramento City Unified and Los Angeles Unified (Regions 3 and 11 in Figure 1), indicate districts with both high citation counts and high concentrations of BIPOC and EMLs. By contrast, districts in central coast locations (e.g., Regions 5 and 8 in Figure 1) with higher BIPOC and EML enrollments were cited less often, as indicated by bluer shades. Inland districts with lower BIPOC enrollment and fewer EMLs received citations more often, as shown on Map 3 having more districts with dark red shading in those locations (e.g., Region 7). These results suggested a threshold of BIPOC enrollment—at the intersections of locale and location—at which the risk of citation decreased.

Finally, we descriptively examined citation types. Most Indicator 4 citations involved overrepresentation of Black students in exclusionary discipline, and Indicator 9 citations most often involved Latine overrepresentation in special education overall. Across the dataset, Indicator 10—disproportionality in specific disability categories—was the most common citation type. The greatest number of Indicator 10 citations was for overrepresentation of Latine students in SLD, followed by White students in Emotional Disturbance (ED), White students in Autism, and Black students in ED. These findings shaped our qualitative analysis (Creamer, 2018), in which we explored policy enactment, race, and disability in context across these types (Cruz et al., 2024; Fish, 2019).

Qualitative Findings: District Plans and Local Perspectives

Phase 2 focused on how cited districts understood their disproportionality. We analyzed documents to examine local framings of root causes and solutions, attending to how perspectives shaped districts’ responses. Documents indicated how district teams negotiated and reported their mandated responses and whose perspectives were foregrounded in their analyses and planning. In large-city districts, teams tended to include general and special education leadership, select site principals, teachers, school psychologists, policy personnel, and technical assistants. One plan noted that the “District Superintendent [and] Special Education Director [were the] decisionmakers responsible for identifying individuals who will direct the day-to-day activities related to the development of the CCEIS plan” and were therefore “generally accountable for overseeing development and successful implementation.” In small-town districts, smaller teams often led the CCEIS plan work, including the regional representative overseeing special education services, along with general and special education administrators. Across the various teaming compositions, district plans depicted negotiations, voices, priorities, and histories related to each area's local context.

(Racialized) Conceptions of the Problem and Solution

Districts’ root cause analyses tended to attribute disproportionality to students, teachers, and/or local policies and processes, varying based on the disability category and student racial/ethnic group implicated in the citation.

“A High-Risk Home”: Latine Students and Deficit Framings

Districts cited for overrepresentation of Latine students in special education—overall and in the SLD category—and often situated the problem in student and family deficits. One teacher attributed referrals of Latine students to their “academic difficulties, behavioral difficulties, global cognitive delays, and a high-risk home environment.” In another plan, one special education administrator stated, “More than half of the students with disabilities who [are] chronically absent are members of families who live in poverty,” and proposed a staff book study about poverty as a remedy.

Elsewhere, district plans echoed Ong-Dean’s (2006) conceptualization of racialized pathways into special education. School psychologists often focused on who initiated a special education referral, contrasting White parents initiating referrals to obtain resources with staff initiating referrals for Latine students based on academic struggles and “low language proficiency.” Racialized framings invoked a high-road/low-road logic (Ong-Dean, 2006): For White students, parent-initiated special education eligibility brought access to resources and accommodations, whereas eligibility for minoritized students brought stigmatizing diagnoses and placements in restrictive settings with limited exit opportunities.

Many CCEIS plans characterized Latine student and family engagement as lacking, with one school psychologist framing families as having “unreasonable expectations for their child” and “want[ing] a free assessment.” Another teacher noted, “The student who continues presenting behaviors and struggles academically is usually the one with who [sic] parent involvement is lacking.” These views alluded to empty-backpack theories (Bal et al., 2014; Cruz & Firestone, 2022), attributing Latine overrepresentation to those students’ insufficient “readiness” for school: “Our middle and elementary schools need more support, more staffing, and more interventions.” Consequently, these districts proposed solutions focused on remediation and behavioral compliance, such as hiring more paraprofessionals, adding academic supports, and implementing reading- and language-development curricula in “high-need” elementary schools.

“Difficult, Defiant, Destructive”: Black Students and Myopic Approaches

Districts cited for Black student overrepresentation, often in the ED category or discipline, tended to attribute disproportionality to practitioner bias. Teachers and administrators most often implicated individual-level racism, emphasizing how practitioners perceived Black students, rather than structural framings of racialized systems that reproduce disadvantage (Bonilla-Silva, 2021). One focus group participant stated, “It comes down to how we view the Black children and their feelings,” adding that educators often described Black students as “difficult, defiant, destructive.” Teachers and school psychologists attributed these disparities to practitioner bias—“We are tackling African American students and looking at them from one lens, through the lens of White male authority”—and cultural mismatch: “As a White male, I don't always understand the behaviors.” Yet, when plans included Black families’ perspectives, parents often described interpersonal racism. One parent described how school staff told her son, “You can't speak ‘proper’ English.” At times, Black families reflected structural understandings of root causes, describing, for example, regional dynamics contributing to racial isolation, a perspective we revisit in the joint display analysis.

Notably, school-employee perspectives reflected in these plans situated anti-Black racism at the individual—rather than the structural—level (Bonilla-Silva, 2021; Mosley et al., 2021), and proposed solutions tended to be similarly narrow in scope. For example, many described planning to incorporate culturally responsive add-ons to existing programming that would address students’“culturally mediated behavior,” and one team proposed renaming School-Wide Positive Behavior Interventions and Supports as “Culture and Climate Teams.” Others suggested professional development focused on changing teachers’ attitudes (e.g., anti-racism training) given that “our African American kids are disproportionately penalized and criticized because their behavioral expression is different from the White kids.” In this way, these plans remained rooted in deficit logics, focused on individual attitudes or programs rather than systemic conditions. Although some acknowledged racial disparities and anti-Blackness, planned solutions failed to interrogate the broad structures that sustain them.

“Organized, Safe, Structured Learning”: White Students and Systemic Solutions

In contrast, districts cited for overrepresentation of White students tended to frame disproportionality as a systemic issue requiring systemic solutions. Rather than attributing disparities to family deficits or individual biases, suggestions tended to focus on enhancing inclusivity across general learning environments. These plans called for “better support programs,” training “all teachers on how to work with all students in the classroom regardless of their assumed label,” and centering school culture improvements from a universal standpoint. For instance, a member of one district leadership team noted, “If [general education] supports were provided, a student who qualifies for ED would not necessarily need to be in [special education],” noting that significant disproportionality “doesn’t only ‘live’ in SPED [special education].” In doing so, these educators implicitly—and at times explicitly—critiqued IDEA's fraught labeling logic, framing disproportionality as a symptom of systems failure rather than a reflection of individual pathology.

Across cases, we observed a racialized pattern in how districts understood and addressed disproportionality. When BIPOC were overrepresented, plans focused on identifying and fixing student and family deficits, yet, when White students were overrepresented, plans emphasized organizational change and systemic support. We explored this tension in Phase 3.

Findings Integration: A Spatial-Causal Mosaic of State Citations

In the final phase of analysis, we integrated qualitative and quantitative findings to answer the third research question using joint display analysis. Through that systematic integration process, we considered the various mosaics (Johnson et al., 2019) that emerged at the intersections of policy structure, local enactment, and spatial context. We constructed multiple iterations of district-specific joint displays, visualizations that juxtaposed quantitative findings (e.g., composition, citation patterns, and spatial characteristics) with qualitative findings (e.g., framings of root causes, local initiatives, and references to spatial histories such as gentrification or segregation). This side-by-side alignment—in which we considered findings at comparable levels of aggregation—allowed us to examine the convergence of structural forces and local interpretations (Love & Corr, 2022). Because we juxtaposed findings from the two strands in districts’ joint displays, we considered whether they confirmed, expanded on, or complicated one another. We then grouped districts based on the degree of cross-strand coherence for further analysis (Firestone et al., 2024). We next present examples from this phase to illustrate how findings integration catalyzed new understandings of local policy enactment across spatial contexts.

Location and Context: Gentrification and Urban Displacement in Three City Districts

In Figure 4 we present three districts’ joint displays to illustrate how integrating findings expanded our understanding of spatiality and local context. Situated in different geographic locations, each district shared a locale type: large city. Districts 1 and 2 were in urban-center locations with thriving technology, business, and entertainment industries, and both were cited for overrepresentation of Black students in behavior-based indicators, including ED and discipline. District 1 featured relatively stable and evenly distributed population diversity, whereas District 2 featured a growing Latine population. District 3, in contrast, was in an agricultural location and featured a higher population of Latine students and EMLs. As we generated these three joint displays, we noted that they reflected the mono-methodologic findings—District 3’s regional difference came with a different citation pattern, and its documents reflected distinct framings of disproportionality.

Figure 4.

Three city districts’ joint displays: The salience of space on disproportionality.

As we iterated districts’ joint displays, we juxtaposed citation patterns with qualitative themes reflecting local context. Across the dataset, large-city districts located in urban centers (e.g., Regions 3, 4, and 11 in Figure 1) cited for Black student overrepresentation often had plans that attributed their disproportionality to anti-Black racism within the education system and, at times, the system itself. In particular, Black families’ voices pointed to regional dynamics—most notably housing displacement, rising costs of living, and the erosion of historically Black neighborhoods—that contributed to racial isolation, reducing Black students’ numerical representation while intensifying their visibility in discipline- and behavior-based citations. Bringing these strands together expanded our understanding of the location–composition interplay. As we extended beyond the locale analysis, we connected regional industry and history to demographics and how that confluence shaped citation patterns and responses depicted in the qualitative data.

For example, in Districts 1 and 2, Black families—the group impacted by the disproportionality—described local dynamics that had led to loss of community over time. In District 1, these included a local history of Black neighborhoods having been severely impacted by the 2008 recession and subsequent within-district segregation and/or gentrification due to housing price increases (e.g., Chapple, 2017; Hooks, 2022), although our data often displayed this in general terms. ⁵ For instance, a parent in this district noted that a root cause of disproportionality was that “There is rac[ial] and ethnic segregation in our school system.”

District 2 had a recent history of severe and increasing income inequality and urban displacement, leading to demographic and economic shifts (e.g., Mujahid et al., 2019; Richardson et al., 2019), and parents described these dynamics as contributing to a lack of institutional support—“[There is] minimal parent liaison support for African American families and students”—and a lack of representation—“African American students represent only 5% of the district population. It's hard for them to find someone that looks like them.” For example, one parent provided an interview describing how “the loss of Black families and communities in [District 2] has resulted in Black youth experiencing cultural isolation within school sites.” This reflected broader demographic shifts, including a decrease in the overall Black population due to displacement and high costs, coupled with systemic issues in these large urban districts (e.g., widening achievement gaps and poor support for Black students).

Black students were a minority in all three large-city districts shown in Figure 4, but only District 3 was cited for something other than overrepresentation of Black students. Because we initially examined these districts alongside one another, this surfaced as a complicating result and motivated us to re-engage with the data. In contrast to Districts 1 and 2, District 3 was in an agricultural location with a large population of farmworker families, and Latine students comprised a greater portion of its enrollment compared with Districts 1 and 2. As quantitative results had indicated, despite sharing the large-city locale classification with Districts 1 and 2, District 3’s location (i.e., agricultural) and composition (i.e., high Latine population) came with a different citation type. Cited for overrepresentation of White students in ED (e.g., Figure 4), District 3’s documents reflected local attributions of disproportionality to systemic problems, such as inconsistent special education eligibility procedures and insufficient access to high-quality learning in general education spaces. Bringing these findings together in a joint display provided additional depth regarding location's influence on citations and local understandings.

To explore connections between spatiality and local perspectives, we revised joint displays to include further information on local context—political leanings, common industries, and sociodemographic histories. We considered how ongoing educational initiatives noted in documents reflected those local characteristics and whether their framings of disproportionality were associated with contextual factors. Building these district-focused joint displays expanded our understanding of the mono-methodologic results, suggesting, for example, that even among districts sharing the same local (e.g., large city), those situated in locations with recent histories of gentrification were more likely to be cited for overrepresentation of Black students and to propose race-evasive educational initiatives. Holding locale constant allowed us to examine how regionally specific political-economic conditions shaped both citation patterns and districts’ interpretations of disproportionality. District 1’s location had a history of Black displacement, suggesting spatial context for descriptions such as the following from a Black teacher:

Beginning early in elementary school there is a positive feedback loop between discipline, attendance, literacy proficiency for our African American students, disenfranchisement among our African American families, and limited cultural proficiency among our teachers and staff. (District 1; Figure 4)

Black families’ descriptions of marginalization across the community and within schools alluded to carceral logics (Meiners, 2016). One Black parent described her son's experience with anti-Black carcerality in their school:

The school did not call me before they called the sheriff. It was traumatic for [my son] and for me to know he had been taken by the police from school and held for 72 hours when he was only in fourth grade. That experience is still affecting him today. (District 2; Figure 4)

In revisiting these plans, we noted Black families’ and educators’ distinct descriptions of isolation and structurally embedded anti-Black racism.

However, policy documents from districts such as Districts 1 and 2 rarely proposed efforts to understand how their sociohistorical contexts positioned Black students. Instead, districts with Black student overrepresentation in behavior-based categories often focused on changing how individual teachers perceived students. Such strategies are insufficient stand-alone solutions to systemic problems, as evidenced in these districts’ repeated citations over time. Their plans rarely proposed building organizational consciousness or transforming educational structures in spaces where Black students were routinely excluded through discipline, labeling, and pathologizing systems (Mosley et al., 2021).

In addition to re-engaging with the data, we returned to the related literature as contradictions surfaced across findings. Some scholars have characterized educational inequities as a residual consequence of a “‘tipping’ of the school's population,” as, for example, “gentrifying parents performing the role of careful investors determined to both contribute value to and acquire value from their school choices,” leveraging education systems in ways that marginalize low-income families of color (Freidus, 2019, p. 1123). Unlike the urban-center industrial contexts of Districts 1 and 2, District 3’s agricultural location presented a context without anti-Black gentrification. Through these juxtapositions of qualitative and quantitative findings in the joint display analysis, results implicated how the historical-temporal context of a location, even across similar locales (i.e., cities), influenced citation patterns and, in turn, how local actors conceptualized root causes and solutions. This represented a meta-inference regarding the salience of space on disproportionality.

City-Suburb Interchange: Spatially Influenced Interpretations in Two Neighboring Districts

As we evaluated coherence across quantitative and qualitative findings—attending to locational influences and framings evidenced in policy documents—we noted that small-city districts often were neighbored by large and midsized suburbs with distinct compositions and citation patterns. In addition to our findings regarding spatially negotiated citation patterns in large-city districts located in differing regions (Figure 4), the joint display analysis of districts in geographically proximal locations presented a context for investigating historical-temporal influences.

Figure 5 shows two neighboring districts in the same coastal location. District A was in a working-class, midsized suburb on the outskirts of an affluent small city, which held District B. Quantitative results had indicated the convergence of composition and location in influencing citation patterns and that suburban districts serving predominantly Latine and EML populations were often cited for White student overrepresentation (e.g., District A; Figure 5). Meanwhile, city districts in neighboring regions with fewer Latine and more White students often had overrepresentation of Latine students in the SLD category (e.g., District B; Figure 5). Qualitative findings had indicated that districts cited for White overrepresentation tended to propose systemic, universal improvements. Developing these districts’ joint displays initially complicated and then—through the integrative analysis—expanded our understanding of these findings, uncovering a narrative of district policy compliance and local perceptions of equity needs.

Figure 5.

Two neighboring districts: Spatially influenced interpretations.

In District A, Latine students comprised nearly the entire population, and the district was cited for overrepresentation of White students in Autism. District documents depicted local educators problematizing the state's risk metric for calculating disproportionality, noting how demographic composition shifts changed the risk ratio's denominator: “The district's enrollment trend over the past 3 years shows that when . . . enrollment increased, students identified as White with Autism decreased.” Local perspectives reflected in documents indicated disagreement with the state's diagnosis of their disproportionality as an authentic educational problem, and proposed solutions emphasized preventative community-building measures and systemic shifts to identify any student needing additional support early on.

Mono-methodologic results had shown that districts cited for overrepresentation of White students tended to propose systemic solutions; noting District A's disagreement with the citation while proposing such systemic solutions motivated us to explore this tension. As we engaged with District A's additional policy documents (i.e., their Local Control and Accountability Plan), it became clear that the district was engaged in a parallel effort, allocating considerable resources to support their Latine, EML, and Mixteco-speaking students and families; for example, “Bilingual Instructional Assistants will offer an extra layer of support for English Learners by supporting instruction, assisting teachers, and contacting parents/guardians.” Yet, in compliance with IDEA, their CCEIS plan did not reflect these efforts, instead describing systemic remedies to rectify overrepresentation of the White students in Autism category. Although not stated in that plan, those systemic improvements would improve universal early learning supports for all students, reflecting a strategic form of compliance.

Neighboring District B provided an instructive contrast. Although both districts were in the same coastal region, neighboring District B was situated in a highly affluent small city, with extensive tourism and winemaking industries supported by District A's surrounding population of primarily less affluent agricultural workers. Latine students comprised around half of District B's student body—a smaller proportion than in District A—and in their documents, District B's educators proposed correcting their overrepresentation of Latine students by supplementing existing programming. Proposed solutions focused on adding targeted interventions and improving district protocols for evaluating Latine students’ eligibility for special education. This suggested an underlying theory that adding on to existing systems with interventions that would remediate EMLs’ skills would reduce their overrepresentation of Latine students in the SLD category. However, the literature has contradicted such approaches—ongoing commitment to existing systems supplemented with individual-student-focused remediation—given that conceptions of culturally sustaining practices frame inequities as symptoms of systems in need of fixing rather than the children (Fergus, 2016; Kozleski, 2020; Voulgarides et al., 2013). Considering the historical-temporal relationship between Districts A and B—in which District A served students of families who had long supported the tourism and viticulture industries concentrated in District B—clarified how regionally stratified labor markets produced divergent district compositions. These compositional differences corresponded with distinct citation patterns and with whether districts framed disproportionality as a systemic issue or as a problem requiring targeted remediation.

In considering these neighboring districts’ joint displays, we expanded our understanding of spatially influenced policy interpretations: Districts proposing systemic solutions when cited for White student overrepresentation may, at times, practice strategic policy compliance to cohere work aligned with locally grounded understandings of educational inequities. In contrast, those cited for Latine overrepresentation—which had fewer shares of Latine students—proposed narrow solutions suggesting persistent deficit perspectives, constructed within the socioeconomics of these locations.

Discussion: Where Place Meets Policy

Our results indicated complex and nuanced ways that spatiality influenced composition, citation patterns, and localized understandings of disproportionality. Through findings integration, we generated two meta-inferences: First, locations’ historical-temporal characteristics related to districts’ citation patterns, shaping local framings of root causes and solutions, indicating the salience of space on disproportionality. Second, although districts had no choice but to fulfill the policy mandate through submitting causal analyses and plans, this compliance bore little relation to actual reductions in disproportionality. Federal policy functioned as an exogenous influence, at times operating in opposition to local perspectives regarding educational-equity priorities. These findings implicated the complex pathways that lead to disproportionality citations, including how racialized ideologies are reproduced in racialized organizations and, in turn, manifest in racialized structures (Ray, 2019).

Experiences of injustice remain embedded in local and historical contexts and built environments (Agnew, 1987; Butler & Sinclair, 2020; Tate, 2008), and those spatial forces influenced local thinking around solutions. Filtered through local systems, these system-embedded mechanisms impact the special education referral patterns that drive disproportionality citation patterns. Integrating qualitative and quantitative findings foregrounded layers of state policy and localized enactment as parts of a dynamic causal mosaic in which “causal concepts are like tiles that, put next to one another, and in the right way, will let an image emerge” (Johnson et al., 2019, p. 144). Regional differences in socioeconomic inequality, educational investment, racial composition, and interpersonal racism in schools intertwine to link race and academic achievement (Merolla & Jackson, 2019). In our findings, these forces intertwined through regionally specific housing and labor regimes that shaped who lived in particular districts, which students became numerically visible in disproportionality calculations, and how districts interpreted responsibility for inequities in their mandated responses.

As such, we considered various causal narratives that districts constructed in response to disproportionality. In several, educators challenged the policy's premise, particularly when citation patterns did not align with local understandings of equity and implicated White student overrepresentation. Perhaps catalyzed by this cognitive dissonance, in these cases educators questioned the special education system itself, highlighting its interpretive vagueness and potential to pathologize students (e.g., “Look at the criteria for determining the eligibility for ED. It is vague and all depends on interpretation.”). Yet, despite recognizing these systemic flaws, few of those districts’ plans proposed substantive, structural changes.

Instead, compliance overruled organizational transformation across the dataset, and recommended solutions largely focused on modifying existing programs, echoing prior research on the procedural nature of IDEA compliance (see Voulgarides, 2018). In some cases, districts aimed to refine referral processes, for instance, by reducing reliance on subjective teacher reports that depend on “on too few adults within the school environment that may hold differing impressions of the student.” Such approaches reflected a myopic, surface-level targeting of a system-embedded phenomenon. Rarely did plans propose efforts to build organizational capacity or pursue multilevel change, instead defaulting to technical fixes that satisfied policy requirements but left underlying inequities intact.

In addition, federal law requires districts to reallocate 15% of their special education funding to address goals outlined in their CCEIS plans. Across our dataset, plans proposed a range of practice-oriented solutions, including scaling dyslexia interventions and launching mentorship programs for “at-risk students,” many of which reflect IDEA recommendations. However, these solutions overwhelmingly focused on technical adjustments rather than systemic transformation, and—despite persistent citations—year after year, most districts remained in procedural compliance without confronting the structural racism embedded in neighborhood schools and district operations. Indeed, IDEA's call for “unbiased assessments and intervention” (IDEA, 2004, § 300.304) has offered little direction for naming or disrupting racialized organizational conditions. As a result, many plans emphasized professional development and culturally responsive tweaks to existing frameworks, such as School-Wide Positive Behavior Interventions and Supports (see Sullivan et al., 2024). Yet, such solutions lack robust empirical evidence for reducing disproportionality (e.g., Cruz et al., 2021; McKinney et al., 2010; Sabnis et al., 2020; Thorius et al., 2014).

Our findings indicated that to truly address inequities, state policy must shift from this compliance-driven approach to one that emphasizes equity-centered, context-responsive work. Policies should acknowledge the pathologizing structures embedded in special education's medical-model origins, address the carceral logics embedded in school discipline systems, and promote inclusive instructional models within district organizations. As such, state policy would treat disproportionality as a symptom of inequality inherent in the system of disability (Bal, 2017) rather than a technical problem.

Lastly, districts consistently proposed a familiar set of IDEA-sanctioned programs and practices, revealing how little critical scholarship beyond the policy has permeated local disproportionality work. Scholars have critiqued IDEA-sanctioned solutions for reinforcing fixed, deficit-oriented perceptions of student identity and promoting one-shot-fix-all endeavors. Our analysis certainly reflected this. Such approaches fail to engage with the spatialized inequities that shape schooling conditions, sustaining a policy–practice divide that districts are unable to bridge under current structures. We saw this in District 1’s CCEIS plan (Figure 4), which focused on Black student overrepresentation in school discipline. In it, one educator acknowledged, “Our system is inequitable by design,” yet the proposed remedies focused only on “shifts in mindset . . . and practice for supporting our students,” deflecting attention from the structural nature of the problem. Individual-level interventions elide the broader geographies of inequality in which schools are embedded, and addressing these entrenched patterns demands transformative redesigns rooted in local histories and spatial realities (Bal et al., 2014).

Limitations

Mirroring past research, districts in town and rural locales were rarely cited (Aylward et al., 2021), and their CCEIS plans were often unavailable. Future research must explore how rural schools experience citations, particularly because rural was the only locale type in our dataset cited for overrepresentation of American Indian/Alaska Native students, and we were unable to obtain the associated CCEIS plans. An additional limitation was that we did not include metrics for gentrification, urban displacement, or population shifts beyond the demographic information in our longitudinal dataset; these emerged as salient factors in the qualitative phase. Our document analysis indicated that historical, economic, and demographic shifts affected how districts experienced citations, and our joint display analysis clarified how the type of citation (e.g., White students in Autism category) had profound implications for local actors’ understandings of the problem and appropriate solutions.

In addition, our qualitative analysis was limited to policy documentation. We did not interview practitioners firsthand, and we did not observe implementation in schools. The documentation we examined was, from a policy perspective, performative. As such, our results may not reflect true beliefs or actual practices but rather performative components of compliance presented to the state. Future work should examine these findings against implementation in schools and districts. Lastly, although we did analyze how districts allotted their budgets toward solutions, we did not conduct a fiscal analysis of each district's spending. This was beyond the scope of our study because our research questions focused on the convergence of geography, policy, and local understandings. However, these factors certainly shape fiscal decisions, and future work should attend to districts’ budgetary decisions, because redirecting 15% of their federal budget to address disproportionality is a substantive aspect of CCEIS policy (Center for IDEA Fiscal Reporting, n.d.).

Conclusions and Future Directions

Prior studies of single suburban districts have shown that disproportionality citations are mediated by local historical, spatial, and sociocultural contexts (e.g., Tefera et al., 2023). Expanding the existing work, our mixed-methods analysis revealed how spatialized forces drive disproportionality, including district composition, regional histories, and place-specific dynamics such as gentrification. Notably, districts proposed systemic transformation primarily when White students were affected, suggesting that such responses may, at times, reflect strategic rather than substantive compliance. Despite annual citations, most districts continued to be flagged for the same patterns, suggesting that policy adherence alone does little to disrupt entrenched inequities. These findings underscore the need for a revised IDEA policy—one that equips districts to center their local context and pursue systemic change.

Finally, future research should provide cross-district and cross-state analyses to build a fuller causal mosaic of disproportionality (Johnson et al., 2019), clarifying both drivers and remedies. There is also an urgent need for studies that surface effective approaches that districts can use to interrogate the historical, temporal, and geographic roots of educational inequities and to build the organizational capacity to respond (e.g., Bal et al., 2014). Locally grounded, equity-centered processes hold promise for aligning district-level action with the systemic forces identified in the research. Ultimately, creating the conditions for transformation will require the field to foster multidirectional communication across research, theory, policy, and practice—and to confront the persistent histories of inequality that structure our contemporary schools.