Discourse semantics. The gender language parameter: a research case in the semantics of sexism
Bibliographic record
Abstract
El presente artículo, siguiendo la corriente investigadora de la Sociolingüística feminista en América del Norte (EE.UU., Québec y Montréal), estudia, desde el lenguaje de género, el sexismo lingüístico, tanto sintáctico como léxico, su caracterización gramatical, y aporta como novedad el estudio del sexismo semántico aplicado a cinco variantes discursivas, con estrechos lazos culturales entre ambas orillas (española; atlántica: argentina, panameña, mexicana; y mediterránea: marroquí), con el fin de ver cuáles son los universales antropológicos que el análisis pragmático y textual ofrece a través de tres principios semánticos: 1. el significado implícito por inferencia; 2. la oposición de antónimos explícitos: sintagmáticos y paradigmáticos; 3. el contraste o antítesis de términos homónimos y polísemos. El procedimiento de reconstrucción de significado que se ha seguido es el siguiente: a. significado implícito inferencial -› b. oposición de antónimos explícitos sintagmáticos -› c. contraste o antítesis entre diferentes significados de términos homónimos -› d. contraste o antítesis entre diferentes acepciones de términos polísemos -› e. oposición de antónimos explícitos paradigmáticos .
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".