Transformations in Agricultural Non-waged Work: From Kinship to Intern and Volunteer Labor: A Research Brief
Bibliographic record
Abstract
What is the relationship between unpaid and non-waged work and the survival, and even growth, of small- and medium-scale farms? This research brief examines this question through examining the growth of internships and volunteer positions (non-waged work) on ecologically oriented farms, with a focus on trends in Ontario, Canada. Through reporting on the qualitative and quantitative findings of our research, we track the decline of family labor throughout the broader agriculture sector and the emergence of new forms of non-waged work on ecological farms. We focus on the continuities and changes at play in shifting forms of farm work and discuss the new forms of knowledge exchange occurring on farms, the precarious economic situation of many farms, and the gendering of non-waged work. We conclude the brief by raising several challenging questions regarding the politics and sustainability of farmers' dependency on interns and volunteers. See the press release for this article.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".