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Record W3118942295 · doi:10.3917/comla1.206.0111

La loi de Zipf 70 ans après : pluridisciplinarité, modèles et controverses

2020· article· fr· W3118942295 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommunication & langages · 2020
Typearticle
Languagefr
FieldComputer Science
TopicAdvanced Text Analysis Techniques
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsZipf's lawNominationHumanitiesPhilosophyMathematicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Il y a cinquante ans, la revue Communication et Langages consacrait un article à George Kingsley Zipf 1 , chercheur américain peu connu en France. Zipf a mis en évidence des relations mathématiques simples, regroupées sous la dénomination Loi de Zipf, concernant les régularités des fréquences des mots dans un texte. Cette loi a suscité de la part des chercheurs de toute discipline une grande curiosité et alimenté dès les années 1950 des débats dans la communauté scientifique. Qu’en est-il aujourd’hui, soixante-dix ans après la publication de son célèbre ouvrage Human behavior and the principle of least effort ?

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.097
GPT teacher head0.340
Teacher spread0.243 · 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