RUNNERS AND RIDERS: THE HORSEMEAT SCANDAL, EU LAW AND MULTI-LEVEL ENFORCEMENT
Bibliographic record
Abstract
Abstract The 2013 horsemeat scandal shone a bright light on some of the darkest corners of supply-chain governance across the EU, revealing a blind spot in current EU food law. “Beef” frozen-food products were found to contain up to 100% horse. The British and Irish are squeamish about eating horse. Even for those countries where horsemeat is seen as a delicacy, the horse getting into the frozen “beef” was often of poor quality and possibly contaminated with “bute”, a veterinary drug not permitted in food for human consumption. The ability of the EU's regulatory regime to prevent fraud on such a scale was shown to be inadequate. EU food law, with its (over) emphasis on food safety, failed to prevent the occurrence of fraud and may even have played an (unintentional) role in facilitating or enhancing it. Domestic law offered little better protection, thus showing the difficulties associated with the implementation of a multi-level and multi-agency regulatory regime. Beyond the regulatory system, the EU's core Treaty commitment to the free movement of goods may also have laid the ground for complex and opaque supply chains into which unscrupulous traders and middlemen could slip unnoticed.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".