Shifting Societal Attitudes: Examining the Effects of Perspective Taking on Attitudes toward and Derogation of the Poor
Bibliographic record
Abstract
Jessica Louise Wiesea http://orcid.org/0000-0003-3740-3607, Colleen Loomis*bc http://orcid.org/0000-0003-1595-3376 & Terry Mitchellbc http://orcid.org/0000-0002-5506-9641a Centre for Urban Health Solutions, St. Michael’s Hospital, Toronto, Ontario, Canadab Balsillie School of International Affairs, Waterloo, Ontario, Canadac Wilfrid Laurier University, Waterloo, Ontario, CanadaCONTACT Colleen Loomiscloomis@wlu.ca Balsillie School of International Affairs, 67 Erb Street West, Waterloo ON N2L 6C2ABSTRACTPeople tend to hold negative attitudes about and derogate those in poverty to varying degrees, often relying on indivi-dualistic explanations of poverty that largely ignore systemic sources. This study (N = 208) examined a perspective-taking strategy that could be used to reduce distancing behaviors and negative attitudes toward the poor. Perspective takers distanced less and reported fewer negative attitudes than others. An additional finding was that men (n = 57) were more likely to derogate/distance than women, showed greater agreement with personal deficiency explanations for poverty, and reported stronger stereotypic attitudes toward people who are impoverished.
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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 | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".