Bibliographic record
Abstract
Abstract The language at work was an area neglected by linguistics. However, with the development of economy and the advancement of technology for the past ten years, the language at work has undergone profound changes and plays an increasingly important role. In this article we will analyze the functions of language at work and better understand the instrumental , cognitive and social functions of language at work. At the same time through study the language at work make us to rethink profoundly on the existing theories and methods of modern linguistics. Key words: Language in trvail; Functions ResumeLe language au travail etait un domaine oublie par la linguistique. Mais depuis une dizaine d’annees, avec les developpements economiques et les progres technologiques, le langage au travail a connu de profonds changements et joue un r?le de plus en plus important. Dans cet article, on essayera d'analyser les fonctions du langage au travail et de mieux saisir les fonctions instrumentale, cognitive et sociale du langage dans l’entreprise. En meme temps, la prise pour objet d’etude du langage au travail nous oblige a reflechir de nouveau sur les theories et les methodes existant de la linguiste moderne. Mots-cles : Langue au trvail; Les fonctions
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".