Social Citizenship and Disability: Identity, Belonging, and the Structural Organization of Education
Bibliographic record
Abstract
The framing of disability is an ongoing, negotiated discourse in which participants build upon, challenge, and reject the political, social, economic, and cultural influences that lead to constructions of impairment. Experiences of racialization, poverty, immigration, gender, and sexuality juxtaposed against defined institutionalized norms and dominant narratives speak to how disability is not only conceived but also experienced. Drawing upon transnational and citizenship theory, this thesis proposes employing a new framework of analysis, centralizing the experience of social citizenship and belonging as an indicator of broader structural equity. Situated in the field of education, theoretical considerations also explore how growing market fundamentalism shapes public schools and contributes to the systematic exclusion of poor and racialized students through mechanisms of disablement such as reduced academic programs and special education placement. \n \nThis body of work includes three separate, but related, studies exploring historical and current incidences of institutional exclusion. In particular, the nuanced relationship of exclusion to race, class, gender, generational status, and sexuality, complicated with the identification of impairment, is explored. One of the most profound findings of this research is that, although there is much discussion in Disability Studies of the construction of impairment labels, this is the first quantitative analysis to substantiate these claims. Results also indicate that the classroom represents the most stratified space in which student groups defined by race, exceptionality, class, and generational status experience the greatest sense of exclusion. Evidence shows that employing a lens of citizenship and belonging is an authoritative tool in identifying the existence of inequities distributed among myriad identity groups. Furthermore, evidence lends credence to the notion that identification of disability is intimately linked to race, gender, and class contexts. \n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".