Bibliographic record
Abstract
Preliminary results from an experimental study of readers’ perceptions of lexical cohesion and lexical semantic relations in text are presented. Readers agree on a common “core” of groups of related words and exhibit individual differences. The majority of relations reported are “non-classical” (not hyponymy, meronymy, synonymy, or antonymy). A group of commonly used relations is presented. These preliminary results indicate potential for improving both relations existing in lexical resources, and methods dependent on lexical cohesion analysis.Les résultatspréliminaires d’une étude expérimentale sur les perceptions des lecteurs au sujet de la cohésion lexicale et des relations lexicales sémantiques de textes sont présentés. Les lecteurs s’entendent sur un « noyau » commun de groupes de mots reliés et présentent des différences individuelles. La majorité des relations indiquées sont « non classiques » (ni hyponymiques, méronymiques, synonymiques ou antonymiques). Un groupe de relations couramment utilisées est présenté. Ces résultats préliminaires indiquent le potentiel nécessaire pour améliorer aussi bien les relations existant dans les ressources lexicales que les méthodes dépendant de l’analyse de la cohésion lexicale.
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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".