MétaCan
Menu
Back to cohort
Record W3122151115

The Matrix of Law: From Paper, to Word Processing, to Wiki

2014· article· en· W3122151115 on OpenAlexaff
Florian Martin-Bariteau

Bibliographic record

VenueSSRN Electronic Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsUniversité de MontréalUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesPolitical scienceArtSociology
DOInot available

Abstract

fetched live from OpenAlex

Il y a quinze ans, Francois Ost proposait le modele du “traitement de texte” pour analyser et comprendre l’evolution de la production legislative depuis les debuts de la societe de l’information. Cet article entend presenter et discuter la pertinence et l’actualite de ce modele dans le contexte actuel. Mettant en lumiere la regulation comme nouvelle logique de gouvernance dans les societes postmodernes au droit reseautique, cet article insiste sur le phenomene de marketing legislatif. Confirmant que le droit est dorenavant en etat de transit et que le cadre juridique a perdu sa coherence, cet article propose d’aller plus loin que la pensee de Francois Ost avec le modele du Wiki, une utopie envisagee comme nouveau paradigme pour comprendre et produire le droit de la societe du XXIeme siecle.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.008
Science and technology studies0.0030.013
Scholarly communication0.0190.027
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.003

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.

Opus teacher head0.007
GPT teacher head0.285
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicLaw in Society and CultureFrench-language works237,207