Discrimination at every turn: An intersectional ecological lens for rehabilitation.
Bibliographic record
Abstract
PURPOSE: Intersectionality has been increasingly prevalent in the rehabilitation literature. It has been warned, however, that there can be a flattening of intersectionality should social scientists exclude the various systemic paradigms which contribute to, and sustain, marginalization. In seeking a remedy to this issue, the article establishes an intersectional ecological framework for use in rehabilitation psychology. Using Bronfenbrenner's ecological systems theory, the article posits a framework both visually and conceptually, that can be used to discuss the intersection of identities in each system. DESIGN: The current article is a review of literature about intersectionality, disability, and discrimination, for the purpose of establishing a gap in theory that makes the current paper necessary. RESULTS: The establishment of an intersectional ecological framework for use in rehabilitation psychology and its related fields. The newly developed framework is then exemplified using discrimination. IMPLICATIONS: The intersectional ecological framework provides myriad opportunities for researchers, practitioners, and educators. The ability to theoretically discuss intersectionality through the lens of ecological systems theory will allow for thorough work in this area. Specifically, this framework will allow researchers to consider multiple systemic levels in exploration of identity-related issues for individuals with disabilities and provides a way for practitioners to see the complicated intersections individuals are experiencing at any given time. Ultimately, this framework has the potential to improve much of the understanding and treatment of people with disabilities. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.015 | 0.057 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".