Intersecting Identities: Exploring the Interplay of Race and Disability in Employment Support Systems
Bibliographic record
Abstract
Employment supports have traditionally been available to help reduce barriers to employment, promote workforce participation, and empower individuals to achieve their career goals. However, there is very limited knowledge regarding the intersection of race and disability and how it can impact the delivery of employment supports for racialized disabled job seekers and workers. To address this gap, this thesis includes a scoping review and a qualitative study to understand the impact of race and disability in employment support systems. The scoping review consists of 73 studies and a grey literature search on vocational rehabilitation (VR). Data was extracted and thematically analyzed to synthesize the existing knowledge about VR services currently in place for racialized disabled job seekers and workers. The findings highlight the disparities in accessing VR and its delivery in the United States. The included studies reported lower acceptance rates to enter VR programs, and lower probabilities of a successful exit. These findings suggest the importance of service providers conducting comprehensive assessments to determine the unique requirements of each job seeker and worker to customize their supports accordingly. To capture the varied lived experiences and perspectives while navigating the Canadian employment support systems, a qualitative interpretive descriptive study was conducted. In-depth semi-structured interviews were performed with racialized disabled job seekers and workers, service providers, and employers. Interviews were thematically analyzed to identify common themes and patterns about the impact of race and disability in employment supports. The employment support process, as reported by the participants in this study, was identified to be inflexible and bounded by multifaceted structural, organizational, and attitudinal challenges that intersect across race, disability, and employment. Overall, an intersectional approach that is targeted, flexible and inclusive of changes and strategies is needed to create a more equitable employment landscape that better supports racialized disabled workers.
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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| 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".