Animals and Development: A Case Study of the Canadian International Food Security Research Fund
Bibliographic record
Abstract
Food animals such as cattle, chickens, goats, fish and bees are central to Canada’s international development initiatives which are attempting to eradicate global poverty, hunger, and foster sustainable development. Yet, despite the economic and social prevalence of these programs globally, the animals in these projects are often only counted as economic units; their relationships with the people and environment they interact with, and their welfare are left invisible and unaccounted for. This research centres the animals at the heart of Canadian international development interventions. Through a mixed methods approach, this research documents the historic roles of animals in Canadian development interventions, both domestically and globally; the actors, roles and representations of animals through a contemporary case study of the Canadian International Food Security Research Fund (CIFSRF); and looks to the future of animals in development by interrogating the synergies and tensions of the implementation of animal welfare paradigms such as the One Welfare framework. This interdisciplinary research unites animal geographies and development studies to provide scholarly insights on global animal-human relationships, animal welfare, and global well-being to inform future animal-human practices in development.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.042 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".