<i>Sweet Charity</i> , revisited: Organizational responses to food insecurity in Hamilton and Toronto, Canada
Bibliographic record
Abstract
In the last two decades, emergency food provision (e.g., food banks, meal programmes) has become an increasingly institutionalized form of hunger relief. Critiques of the emergency food system, as articulated by Poppendieck’s 1998 book Sweet Charity?, suggest that such programmes are unable to cope with growing hunger in a meaningful, stable, efficient, or culturally appropriate way, and that they may facilitate government retrenchment. Meanwhile, popular attention has increasingly focused on the environmental and social costs of our globalized industrial food system, and efforts to challenge it (e.g., urban fruit gleaning, chicken rearing) are becoming widespread. These efforts have drawn new kinds of organizations into the world of food (in)security. Drawing on organizational documents and key informant interviews, this paper examines how emergency food provision is changing because of the rise of ‘community food security’ discourse and practice in the period since Sweet Charity? Findings suggest that emergency food providers have responded to critiques in partial and incongruent ways. Organizations face structural constraints that curtail their ability to reorganize, while new kinds of organizations are engaging in community food security projects, both challenging and reinforcing the charity food model in ways that have relevance for progressive (food) organizing more generally.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.037 | 0.011 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".