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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".