Los masculinos no tan “genéricos”: estudios empíricos sobre interpretaciones en español y en francés
Bibliographic record
Abstract
En el debate sobre la validez del uso de los llamados “masculinos genéricos”, los posicionamientos académicos suelen hacer referencia a la interpretación de estas formas, generalmente sin citar estudios empíricos que examinen dichas interpretaciones. Estos estudios empíricos, que suelen ser dejados de lado en el debate, buscan examinar mediante experimentos o encuestas si las formas masculinas referentes a seres humanos generan una interpretación específica (referente únicamente a varones) o genérica (referente a seres humanos de cualquier sexo). El presente artículo de revisión propone remediar esa falta de atención a los aspectos empíricos del debate, revisando primero la literatura existente sobre el español y luego comparándola con la que trata datos del francés, conformando así un aparato de evidencia empírica adecuado para la discusión y comprensión de los debates actuales sobre (por ejemplo) la pertinencia o no del lenguaje inclusivo.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".