Bibliographic record
Abstract
Motivated reasoning (MR) refers to the influence wishes, desires, and preferences have on individuals’ cognitive processes including assessing, constructing, and evaluating. Research on MR indicates that individuals have the tendency to accept favorable information and be dismissive or critical of threatening information. Such tendency influences the choice of newspapers and other media sources they consume and the content within these sources they read. MR is found to impact partisan affiliations and to lead to attitude polarization inequality, enhanced discrimination and injustice. Certain discussions linked to disabled people lend themselves to MR such as the origin of disablement (the body, the environment, or both) and whether the characteristic that makes one being classified as a disabled person is a deviation or a variation (see discussions around Deaf culture and neurodiversity). MR enables a polarization between the different views evident. Nearly all aspects and impacts of MR influence the living situation of disabled people. As such the purpose of our study was to investigate how the academic literature around MR engaged with disabled people. Using SCOPUS and the 70 databases of EBSCO ALL we found only two academic articles that directly covered MR in relation to disabled people. We use the issue of evidence generation in academic literature and newspapers as one example for discussing the impact of MR on disabled people. We suggest some actions that should be taken in relation to MR and disabled people.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".