Bibliographic record
Abstract
Sex differences are a topic of perennial interest in our society. But it is not generally understood that most sex differences are the result of social, not biological. In the present article, the differences between women and men are presented from the angle of sociolinguistics. During the analysis, the writer emphasize on the three following aspects: 1) Different diction; 2) Different syntactic approach; 3) Different communicative strategies. We hope that through the study a better understanding of the differences between the genders can be achieved. Linguistic differences are merely a reflection of social differences, and as long as society views women and men as different—and unequal—then differences in the language of women and men will persist. Key words: gender, sociolinguistics, communicative strategies, syntactic approach Resume: La difference de sexe entre les etres est un sujet de conversation eternel de notre societe. Mais le commun des mortels ne comprennent pas le fait que les origines de cette difference sont d’ordre plutot social que physiologique. Dans l’article present, l’auteur met l’accent sur trois points : premierement, la difference de choix du terme ; deuxiemement, celle de syntaxe, troisiemement, celle de technique de communication. Nons esperont que nos recherches peuvent contribuer a une meilleure connaissance de la difference de sexes. La difference langagiere n’est qu’un miroir de la difference sociale. Tant que nons voient les femmes et les hommes d’un oeil different ou inegal, cette difference de langue subsistera pour toujours. Mots-Cles: sexe, linguistique sociale, moyen de communication, syntaxe
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".