Situating Food Industry Influence: Governance Norms and Economic Order Comment on "‘Part of the Solution’: Food Corporation Strategies for Regulatory Capture and Legitimacy"
Bibliographic record
Abstract
Lacy-Nichols and Williams provide important new insights into the ongoing contest over policy space and consumer behavior. I attempt to situate these insights in relation to government mandates and governance norms and situate these norms and mandates in the prevailing economic order. This approach is necessary to understand how corporate practices persist and why governments are receptive to the approaches outlined in the analysis conducted by Lacy-Nichols and Williams. This approach can help explain why governments are often receptive to corporations positioning themselves as ‘part of the solution’. Governments want strong economies and big food positions itself as contributor to this end. The point I attempt to articulate is that we often conceive of corporate power as power over, while I suggest that corporate power is rather power within and through a system that is oriented towards profits and economic growth.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.065 | 0.033 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".