Bibliographic record
Abstract
The Food: Locally Embedded, Globally Engaged (FLEdGE) SSHRC-funded Partnership has deep roots in relationships developed over time among academics and community-based practitioners. FLEdGE emerged from community-driven research in Ontario on food hubs and community resilience dating from 2010. From there it expanded to include seven research nodes across Canada and three thematic international working groups, with over 90 researchers, students, and community partners involved in the project. As a multi-institutional project, FLEdGE has nodes in British Columbia (Kwantlen Polytechnic University)/Alberta (University of Alberta), Northwest Territories (Wilfrid Laurier University), northern Ontario (Lakehead University), eastern Ontario (Carleton University), southern Ontario (Wilfrid Laurier University; University of Guelph; University of Waterloo); Quebec (McGill University; Dawson College); and Atlantic Canada (Dalhousie University; Carleton University). There are two or more lead researchers in each node, typically from different disciplines and several community partners in each node. In this way, FLEdGE branched out to include more than 90 partners and collaborators.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.009 |
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".