Bibliographic record
Abstract
Canada’s food system has evolved under pressure to constantly produce more and do so more efficiently. However, the drive for increased productivity has also led to rising levels of food loss and waste. In Canada, over half of our annual food supply is discarded. Wasted resources, economic costs, pollution and growing numbers of citizens who are food insecure underline the importance of tackling this critical public policy issue. The aim of this paper is to better understand food loss and waste in Canada’s food system and offer suggestions for policy action. Canada’s food system is interconnected and food loss and waste occur at every level of the supply chain. They are the result of multiple and cumulative activities and the economic, social and environmental impacts are considerable. Consumers and businesses fail to adequately measure and account for the costs of waste and this is a reflection of how our society values food. There has been a general disregard for food loss and waste in the pursuit of maximizing output/economic growth, meeting market demands and keeping food prices low. COVID-19’s impact has shed light on the strengths and vulnerabilities of Canada’s food system. Disruptions in our supply chains garnered media attention and food security concerns became top of mind for many Canadians. Diverting food can help alleviate food insecurity but it can also serve an important role in reducing food waste. However, there are key challenges to facilitating food rescue that have been highlighted and exacerbated over the last year, including lack of infrastructure and co-ordination, misconceptions about food safety and worries related to cost and liability. Reducing the problem of food loss and waste in Canada’s food system will require a unified strategy and committed leadership. Policy action should be directed at enhancing measurement, education, innovation and policy reform. Reducing avoidable loss and waste through policy measures that enable prevention and diversion will ultimately strengthen our food system by wasting fewer resources, finding new economic opportunities, preventing environmental damage and alleviating food insecurity.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".