Bibliographic record
Abstract
This manuscript utilizes data from policy stakeholder interviews and a systematic search of government websites to identify how the federal, provincial, territorial, and municipal governments in Canada address food loss and waste (FLW) and how stakeholders interpret jurisdiction over this issue. The findings show that government policies related to this issue represent a patchwork of disparate and overlapping actions that have been enacted by governments at different levels and across a variety of departments and agencies (e.g., environmental, agricultural, economic). Of these policies, only a few were identified as having the explicit objective to reduce the generation of this waste and/or divert it from landfill. Most policies, in fact, had non-FLW related objectives (e.g., to improve the profitability of the agricultural sector), but still had a potential or actual impact on the generation and/or management of this type of waste. Despite it being unclear who has jurisdiction over FLW in the country, an examination of interview transcripts reveals that policy stakeholders have limited views of which government entities have the authority to address FLW. This manuscript argues that the lack of jurisdictional clarity presents a barrier to a more comprehensive governance of FLW. While it may be possible to clarify who has jurisdiction over this issue, this manuscript contends that policy stakeholders need to rethink their understanding of jurisdiction itself. This manuscript operationalizes Valverde’s “work of jurisdiction” to present an alternative way to interpret jurisdiction that opens new possibilities for the governance of FLW.
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 | 0.083 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.022 | 0.113 |
| Scholarly communication | 0.019 | 0.032 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".