Bibliographic record
Abstract
Political philosophers’ prescriptions for poverty alleviation have overlooked the importance of social movements led by, and for, the poor in the global South. I argue that these movements are normatively and politically significant for poverty reduction strategies and global justice generally. While often excluded from formal political processes, organized poor communities nonetheless lay the groundwork for more radical, pro-poor forms of change through their grassroots resistance and organizing. Poor-led social movements politicize poverty by insisting that, fundamentally, it is caused by social relations of power that exploit and subordinate poor populations. These movements and their organizations also develop the collective capabilities of poor communities in ways that help them to contest the structures and processes that perpetuate their needs deprivation. I illustrate these contributions through a discussion of the Landless Rural Worker’s Movement in Brazil (the MST), a poor mobilization organization in Bangladesh (Nijera Kori), and the slum and pavement dweller movement in India. Global justice theorizing about poverty cannot just “add on” the contributions of such struggles to existing analyses of, and remedies for, poverty, however; rather, we will need to shift to a relational approach to poverty in order to see the vital importance of organized poor communities to transformative, poor-centered poverty reduction.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".