From passerby to ally: Testing an intervention to challenge attributions for poverty and generate support for poverty‐reducing policies and allyship
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it