La loi de Zipf 70 ans après : pluridisciplinarité, modèles et controverses
Bibliographic record
Abstract
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 ?
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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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".