Bibliographic record
Abstract
Welcome to the Food Law Workshop, a virtual space for research and resources on food and agricultural law in Atlantic Canada.\nWhy a “workshop”?\nA workshop can be a gathering or event where people come to share and develop ideas, or it can be a place where people go to build things. We like the concept of a Food Law Workshop because it captures both of these–a space to present ideas and projects in progress, with a focus on building applied legal and policy tools to support and help change our food systems.\nWhat is the Workshop?\nThe Workshop doesn’t have a formal structure or a physical home. It is hosted by Jamie Baxter’s research group at the Schulich School of Law (Dalhousie University, Halifax) to publish projects-in-progress and to make ongoing work accessible to people and organizations involved in food. A couple of key ideas guide this work: Each project builds from the core idea of food law as part of a social-ecological system. The work tends to be community driven and aims to respond to the needs of those working with, supporting or advocating around food. Good data are important for good food law and policy, but these data and the ways we analyze them should be transparent and accessible.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.060 | 0.006 |
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".