Gregory Shaffer, dir, Transnational Legal Ordering and State Change, New York, Cambridge University Press, 2013
Bibliographic record
Abstract
L'auteur, des la premiere page, introduit son sujet en affirmant ceci : « Legal norms in almost all domains of law circulate around the globe »1. Le ton est ainsi donne par cette phrase qui resume bien le phenomene sur lequel Gregory Shaffer souhaite instruire le lecteur par le biais de son ouvrage Transnational Legal Ordering, un recueil d’articles compiles portant sur un sujet dont il avait deja ecrit en 2011 : le processus de dissemination juridique par les regimes transnationaux et son effet sur les Etats2. Ce professeur de droit et de sciences politiques a l’Universite du Minnesota a deja ecrit plusieurs livres et articles sur les theories juridiques et l’Organisation mondiale du commerce (OMC)3. En 2011, cinq articles, dont celui de Shaffer, ont ete publies dans un numero de Law and Social Inquiry. C'est deux ans plus tard que ces articles ont ete rassembles, sous la direction de Shaffer, pour etre publies sous la forme d'un ouvrage collectif. Le but de ce livre est d'appliquer le cadre theorique decrit dans l'article4 de Shaffer a des situations concretes introduites par les autres auteurs.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".