Fostering Connections Amidst and Beyond the Pandemic: Community-Based Research and Food Sovereignty in Fredericton, New Brunswick
Bibliographic record
Abstract
Although there is a strong call for engaged and decolonized research in cultural anthropology, very few structures are in place in universities to train and encourage graduate students in carrying research projects that take up these challenges. Born from the successful research collaboration between a Master’s student and Hayes Farm, a community farm in New Brunswick, this thesis aims to prove that change in research practices is both needed and possible. The research team used a community-based research approach and digital methods, including interviews, surveys and appreciative inquiry, to investigate how the community farm model could foster food sovereignty in New Brunswick. In parallel, a prominent focus was brought to the collaborative research process itself and to the nature of relationships, which are placed at the center of the Hayes Farm’s mission. A feminist ethics of care and an action-research framework were used to make sense of the research’s process and results. Key findings include that a community farm can best advance food sovereignty through its role as a connector and a healing space for the community, and that the relationships that are created and enacted through collaborative research are as much important as the outcomes that it produces in terms of knowledge.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".