Poverty and racism: How we think and talk about poverty reduction matters
Bibliographic record
Abstract
There is a close connection between poverty and racism yet insufficient literature integrates anti-racist praxis in poverty reduction work. We draw here on the first stage of a project that brought together the Saskatoon Poverty Reduction Partnership (SPRP) and the Saskatchewan Anti-Racism Network (ARN) to explore possibilities for better alignment of the advocacy of both organizations. We conducted a media discourse analysis of 462 newspaper articles systematically extracted from grey literature site Factiva on how poverty reduction is framed and how media reportage links poverty and racism in Saskatchewan. We find that very few newspaper articles published on poverty reduction focus on the connections between poverty and racism. Additionally, there are four dominant ways of framing poverty reduction namely: i) the cost framing of poverty reduction ii) the shame and embarrassment framing of poverty reduction iii) the human rights framing of poverty reduction and iv) the root cause analyses of poverty reduction. The cost and shame framings of poverty reduction may further marginalize or de-center those living in poverty compared to the human rights and root cause framings. More explicit connection needs to be made between poverty reduction work and anti-racist praxis for effective advocacy.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.025 | 0.065 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.015 |
| 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".