Bibliographic record
Abstract
Despite the wealth of discussion and ideas on how food systems might change, and all the plans and schemes created to provide solutions to unsustainable food systems, very few researchers have examined the accounting practices that define socio-economic relationships around food. In this article, I show that the imperative for each entity in food supply networks to obtain a discount on costs involved in food supply to survive on very thin margins, inhibits large-scale change. The approach here is introductory, providing an explanation of the accounting issues involved for a non-accounting audience, and an illustrative case study is used to show the embeddedness of always ‘getting a discount’. The case study is drawn from interview data with those involved in intermediary companies and in alternative food distribution in Canada and the USA. The difficulties faced by organisations distributing food on a more local level and the lack of lasting and widespread change despite their endeavours, is shown to linked to the inevitability that they too need to ‘get discounts’ to survive. This interdisciplinary study is important to provide context for sociological thinkers and activists seeking to understand the barriers to change in food behaviours and food strategies.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| 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".