Bibliographic record
Abstract
The contemporary global agrarian regime has altered the patterns of food production, circulation, and consumption. Its efforts towards food security vis-á-vis capitalist modes of mechanized cultivation have produced large-scale climatic and socioeconomic ramifications, including the dispossession of small-scale farmers from their lands and positions in market value-chains. In an effort to improve the dynamics of contemporary agro-food systems, food practitioners and scholars are engaging in critical analyses of land-grabbing, the feminization of agriculture, extractive-led development, and more. However, we argue that there is a gap between Food Studies scholarship and community-based transformative engagement. To support social justice frameworks, our paper calls for an academic paradigm shift wherein learner-centered experiential classrooms bridge academic-public divides and enhance student learning. Through a case-study of urban farming in Calgary, we also explore topics in place-based learning and participatory approaches that acknowledge and integrate Indigenous ways of knowing, doing, being, and connecting. Our paper provides strategies for supporting local food systems through activist scholarship, capacity building of leadership and technical skills in advanced urban farming, and intercultural relationship building. We conclude by evaluating the success of our approach, presenting potential benefits and challenges, and providing recommendations for best practices in food scholarship to support transformative change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.041 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".