Green Trade and Fair Trade in and with the EU:Process-Based Measures within the EU Legal Order par Laurens Ankersmit, Cambridge University Press, 2017, 294 pages
Bibliographic record
Abstract
J’écris ce compte rendu du livre de Laurens Ankersmit, Green Trade and Fair Trade in and with the EU : Process-Based Measures within the EU Legal Order , sur un clavier assemblé en Chine (probablement dans une usine taïwanaise), composé de matières premières extraites aux quatre coins de la planète, et qui s’est retrouvé sur mon bureau après avoir parcouru des milliers de kilomètres de connections logimières extraites aux quatre coins de la planète, et qui s’est retrouvé sur mon bureau après avoir parcouru des milliers de kilomètres de connections logilogistiques et être passé entre les mains de plusieurs intermédiaires. Rien de bien surprenant : la complexité géographique des chaînes de production actuelles n’est pas chose nouvelle. Il en va dès lors de même pour la « nébulisation et la distanciation du commerce » (T. Princen, The shading and distancing of commerce: when internalization is not enough, (1997) 20 Écological Economics, p. 235-253) qui posent question en termes de visibilité et d’accès à l’information, mais également en termes de régulation des conséquences socio-économiques négatives qui découlent d’opérations localisées dans des juridictions plus indulgentes ou moins exigeantes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".