From passerby to ally: Testing an intervention to challenge attributions for poverty and generate support for poverty‐reducing policies and allyship
Bibliographic record
Abstract
Abstract Despite the ubiquity of poverty, its causes remain largely misunderstood and many attribute poverty to individual shortcomings. This stigma not only predicts negative physical and mental health outcomes for those living in poverty, it also psychologically distances them from the economically advantaged. Thus, solutions to the problem of poverty should include efforts to reduce stigma among the economically advantaged, who are often crucial decision‐makers with the power and resources to act as allies. The current research utilized an intensive and immersive intervention designed to challenge the attributions that underpin poverty stigma. In two studies, we tested the effectiveness of this intervention. Results of both studies demonstrate that participation in the intervention consistently predicted more favorable attributions for poverty, and that these changes in attributions, in turn, had meaningful positive effects on participants’ support for poverty‐reducing policies and willingness to engage in poverty‐related allyship.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".