Semántica del discurso: la variable género: una investigación sobre el sexismo semántico
Bibliographic record
Abstract
The present work follows in the wake of North-American (USA, Quebec and Montreal), feminist Sociolinguistic studies. We approach sexism in language from the perspective of gender language, analysing the sexist usage of language and more specifically, the asymmetrical use of the syntax, lexicon and grammatical characterization in Spanish. The novelty of the present research lies in its application to five varieties of Spanish and its analysis of the corresponding discourse variants as well as its treatment of the close cultural links found among both shores (The Spanish-Atlantic one, as regards Argentinian, Panamanian and Mexican Spanish, and the Mediterranean one, as it applies to Moroccan Spanish). Through the ongoing research it is possible to find out the anthropologic universals on which our pragmatic and textual analysis relies; for that, we operate with the help of three semantic principles: 1, implicit meaning / inference; 2, the opposition of explicit syntagmatic vs. paradigmatic antonyms; 3, the contrast or antithesis of homonymous and polisemic terms, through the following chain: a, inferential implicit meaning; b, opposition of explicit syntagmatic antonyms; c, contrast or antithesis among different meanings of homonymous terms; d, contrast or antithesis among different senses of polisemic terms; e, opposition of explicit paradigmatic antonyms.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".