Mapping Ableism: A Two-Dimensional Model of Explicit and Implicit Disability Attitudes
Bibliographic record
Abstract
Nondisabled people often experience a combination of negative and positive feelings towards disabled people. There are often large discrepancies between what nondisabled and disabled people view as positive treatment towards disabled people, with disabled people often viewing nondisabled people’s actions as inappropriate, despite nondisabled people believing they had good intentions. Since disability attitudes are complex, both explicit (conscious) attitudes and implicit (unconscious) attitudes need to be measured. Different combinations of explicit and implicit bias can be organized into four different categories: symbolic prejudice, aversive prejudice, principled conservative, and truly low prejudiced. To explore this phenomenon, we analyzed secondary explicit and implicit disability prejudice data from approximately 350,000 nondisabled people and categorized people’s prejudice styles according to an adapted version of Son Hing et al.’s (2008) two-dimensional model of racial prejudice. Findings revealed most nondisabled people were prejudiced in the aversive ableism fashion, with low explicit prejudice and high implicit prejudice. These findings mirror past research that suggests nondisabled people may believe they feel positively towards disabled people but actually hold negative attitudes which they disassociate or rationalize. Mapping the different ways ableism operates is one of the first of many necessary steps to dismantle ableism.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".