From the “War on Poverty” to the “War on the Poor”: Knowledge, Power, and Subject Positions in Anti-Poverty Discourses
Bibliographic record
Abstract
Anti-poverty discourses are interrogated through a case study of articles on the websites of the Ontario Coalition Against Poverty (OCAP) and The Toronto Star covering a tenant activism campaign. An autonomous media article by OCAP on direct actions to “stop the war on the poor” is compared with an article in The Toronto Star depicting tenant-activists lobbying government in the “war on poverty.” Subjectivity and power relations are analyzed by deconstructing binaries, including deserving/undeserving poor, pride/shame, and dignity/stigmatization. I find productive interdiscursive relations emerging, whereby the two discourses are mutually implicated in creating possibilities for social transformation. I also argue that critical discourse analysis needs to become a more participatory engaged methodology, taking direction from and providing accountability to its research subjects.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.041 | 0.129 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".