Bibliographic record
Abstract
Abstract Food waste has drawn increasing public attention, and the high levels of estimated waste are largely considered to be a failure of our current food system. Recently, economists have begun to weigh in, showing food waste can emerge as the result of a complex equilibrium affected by consumers’ preferences for convenience; expectations about future food prices and availability; food safety concerns; producers’ costs of holding inventory, transportation, and storage; government regulation; and technology. If food waste is a form of inefficiency, there are either strong economic motivations to reduce waste, or unmeasured costs or preferences affecting waste decisions. If consumers have behavioral biases, suffer from information asymmetries, or do not pay the full cost of their waste, there may be a role for government intervention to reduce waste, but most empirical models in the literature have not articulated or quantified the extent of the deadweight loss from the market failures in relation to food waste. In some cases, waste reduction efforts could harm producers if overall demand for food is reduced or harm consumers if overconsumption is encouraged, quality or safety degrades, or supply disruptions occur. Technological innovations, which lower the cost of storage or extend shelf life have the potential to improve both consumer and producer welfare.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".