Fostering A Transformative Shift In Canadian Agriculture By Addressing The Challenges Faced By Farm Women
Bibliographic record
Abstract
Women’s contributions to farming and farm management in Canada have historically been undocumented. And, while much has been said about the experience of women in farming, little has been measured. To replace stereotypes and anecdotes with a foundation of facts, a ground-breaking study was conducted. The study was guided by a national Steering Committee of farm women. Research methodologies include an environmental scan of existing literature and data, in-depth interviews with farm women, and a nationwide survey of farm women. It is the first national study to shed light on farm womens’ crucial involvement in farm management and influencing farm success in Canada. Results reveal women are making indispensable contributions to farming in Canada with a high degree of involvement in virtually every aspect of the operation yet continue to face significant challenges. Furthermore, there is a tremendous diversity of experiences among farm women that require unique solution; not a one-size-fits-all approach. This paper explores the contributions of women and offers a compelling narrative that highlights the experiences of farm women, including their motivations, aspirations, challenges faced, and opportunities to foster a transformative shift in the industry by supporting their unique needs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.046 | 0.012 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".