The Commoditization of Food Waste: A Case Study in the Province of Québec
Bibliographic record
Abstract
Food waste occurs across various levels of the food system, from the stages of production to consumption. Prevention of this waste can help mitigate greenhouse gas emissions and contribute to improving food security. Private actors in Québec have taken an increased interest in wasted food, seeing an opportunity to turn it into profitable products. So-called ‘upcycled foods’, which turn food waste into edible new food products, are an example of one such initiative. While upcycled foods are gaining more social acceptability alongside interest in sustainable diets, this sector of the province’s economy remains nascent and has yet to garner much scholarly attention. In this thesis, I investigated the opportunities and challenges facing entrepreneurs in this sector. To do so, I conducted semi-structured interviews with representatives from six upcycled food companies in the province to ask about their commodity chains and relationships with different actors. My findings show that the most common challenges were those related to social acceptability at initial stages of conception, supply consistency and production volume. The greatest potential for growth appeared to be through partnerships and collaboration with other private and non-profit actors. Most importantly, the biggest influence of interviewed companies on environmental sustainability rested in their ability to redefine waste as a profitable locus for agri-food innovation. This established profitability, in turn, fosters waste consciousness of industry stakeholders, leading to greater engagement and transparency in waste production and mitigation. While my exploratory study is a crucial step in drawing a preliminary profile of this emerging industry in Québec, future research should examine life cycle environmental impacts and social equity dimensions in order to more fully understand the overall sustainability implications of upcycled foods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".