Bibliographic record
Abstract
Disputes over food safety standards –what in the language of trade policy are called sanitary and phytosanitary standards (SPS) – have been at the heart of many transatlantic trade rows between the US and the EU. Examples include the EU bans on the import of hormone–treated beef, on pork treated with growth–promoting additives, or on poultry washed in antimicrobial rinses to reduce the amount of microbes on meat. As a result, the potential impact of the ongoing negotiations to reach a Transatlantic Trade and Investment Partnership (TTIP) free trade agreement between the US and EU on EU food standards has, rightly, attracted a lot of attention and no little anxiety. Opposition to “Chlorhühnchen” has become the rallying–call for anti–TTIP activists in many countries. NGOs argue that “TTIP will sacrifice food safety for faster trade”. Critics highlight possible procedural rules requiring transparency of decision–making and early warning mechanisms which would give interested parties (including of course business firms and lobby groups) the opportunity to comment on planned rule–making which it is argued are likely to lead to ‘regulatory chill’.
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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.031 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 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".