Including Farmers' Voices in the Farm Labour Debate
Bibliographic record
Abstract
While promoting sustainable agricultural, it is important to both address the many challenges to farming sustainably in Ontario and envision a comprehensive food ethic. One of the greatest challenges and unjust aspects of farming pertains to farm labour. Working within a Gramscian perspective, inclusive of food justice and just labour frameworks, this paper will explore whether the systemic factors leading to vulnerable conditions for both farmers and farm workers can be addressed simultaneously. The naming the moment political analysis, inspired by Gramsci, requires the current conjuncture to be explored. This paper presents exploratory research, based on qualitative interviews with nine farmers operating small-‐ and medium-‐scale sustainable farms in Ontario. By including farmers in the discussion, and placing farm labour within the broader food and labour juncture, we can better understand the use of precarious workers in agriculture as indicative of a broken food system. Workers, activists and researchers must continue their attempts to define just farm labour within the current moment to suggest steps toward a desirable future. Recognizing the precariousness of farmers, in addition to workers, and seeking cross-‐sector alliances offers an example of a step towards more just farm labour conditions for all.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.027 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".