Bibliographic record
Abstract
In the near future, Canada will be implementing a national food policy; in doing so, it will be joining a growing number of countries with policies and strategies that address the growing problem of food waste. Food waste is a major economic drain estimated to cost Canada $31 billion dollars annually or $107 billion in true cost, when the costs of wasted water, energy, and resources are included (Gooch & Felfel, 2014). Despite the staggering cost, there is currently a limited number of scholars tackling the issue of food waste in Canada (Abdulla, Martin, Gooch, & Jovel, 2013; MacRae et al., 2016; Parizeau, von Massow, & Martin, 2015). Some of the leading think tanks and research institutions, such as the World Resources Institute (WRI), National Defence Research Council (NRDC), as well as inter-sectoral collaboratives such as Canada’s National Zero Waste Council (NZWC) have identified several priorities to address food waste. Key priorities include, but are not limited to: 1) education and awareness; 2) harmonizing food waste quantification through waste audits and establishing reduction targets; 3) addressing confusion over “best before” labels; 4) incentivizing surplus food donation; and 5) landfill bans on food waste. While these priorities are currently being debated and consulted upon in Canada, several countries around the world have already reached the implementation stage. Canada is therefore in a position to learn from the impacts of policies in other countries with a view to developing a more sustainable, systematic, and just approach to food waste prevention and reduction in Canada.
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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.028 | 0.012 |
| Scholarly communication | 0.025 | 0.013 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.039 | 0.022 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".