The social identity approach to disability: Bridging disability studies and psychological science.
Bibliographic record
Abstract
Although mainstream psychology has received numerous critiques for its traditional approaches to disability-related research, proposals for alternative theory that can encompass the social, cultural, political, and historical features of disability are lacking. The social identity approach (SIA) offers a rich framework from which to ask research questions about the experience of disability in accordance with the critical insights found in disability studies (DS), the source for many of the most compelling critiques of disability psychology research. We review existing research considering the complementary social identity (Tajfel & Turner, 1979) and self-categorization (Turner, Hogg, Reicher, & Wetherell, 1987) theories to support our contention that the disability social category is a significant driving force in the psychological experience of disability and to demonstrate the theoretical utility of the SIA. We suggest that a bridge between the critical epistemological perspectives found in disability studies and the methodological rigor and theoretical breadth and parsimony of a social identity approach is essential for examining the social psychological experience of disability in the 21st century. To conclude we explore the emergent possibilities for research in psychological science that can follow from a social identity approach to disability. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".