Bridging Out of an Impoverished Paradigm: A Qualitative Study to Help Us ‘Rethink Poverty’ Today
Bibliographic record
Abstract
This study explores two poverty training curricula, namely Bridges Out of Poverty (‘Bridges’) and a community-developed curriculum dubbed Rethink Poverty, intended to educate people about the causes, impacts and outcomes of poverty. The research questions posed in the study explore: (a) how are the poverty curricula assessed by participants; and (b) what can we learn about the ways in which poverty training materials are designed and/or delivered that might enhance their relevance and efficacy for community audiences? Employing a thematic analysis of qualitative interviews with participants, several themes emerged, including the importance of workshop facilitation that excludes the ideas surrounding Bridges (a theme dubbed ‘More of the same?); targeting poverty training to populations outside typical health and social service audiences (a theme entitled ‘Going beyond preaching to the choir’); themes related to ‘Observations on the evidence of poverty curricula’ and ‘Perceptions of poverty and debunking myths’; addressing the current ‘(Limited) motivation for action’ on poverty; and how to engage people to increase poverty awareness and advocacy (‘What’s missing’ in poverty training curricula). The discussion outlines key points, based on adult learning theory, for community providers to consider when offering poverty training for community audiences.
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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 | 0.041 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.027 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".