Brecha de género en la universidad, productivismo y tecnologías de la información (Gender gap in the university, productivism and information technologies)
Bibliographic record
Abstract
Resumen. En el mundo, la inequidad laboral entre hombres y mujeres tiende a estrecharse en el último cuarto de siglo, empero, los avances están perdiendo celeridad; la Organización Internacional del Trabajo (OIT, 2016) estima que serán necesarios al menos setenta años para colmar la brecha salarial por género. Desde este marco general de inequidad, este artículo exhibe la situación prevaleciente en materia de trabajo académico en las universidades públicas de México; el análisis se sitúa en el marco de un productivismo que imprime un sello inédito a la organización del trabajo científico y educativo. Se trata de mostrar que ésta lógica operativa encuentra en las tecnologías de la información y comunicación (TIC) un recurso clave para apuntalar las competencias individuales, así como visibilizar los resultados académicos. Se concluye que, en su variedad, versatilidad y potencial, la apropiación estos recursos son la oportunidad para coadyuvar en la reducción de otras diferencias laborales entre géneros en este ámbito profesional. Abstract. In the world, labor inequality between men and women tends to narrow in the last quarter of a century, however, progress is slowing down; the International Labor Organization ´(ILO, 2016) estimates that it will take at least 70 years to close the gender pay gap. From this general framework of inequity, this article shows the prevailing situation regarding academic work in public universities in Mexico; the analysis is situated within the framework of a productivism that gives an unprecedented stamp to the organization of scientific and educational work. The aim is to show that this operational logic finds in information and communication technologies (ICT) a key resource to underpin individual skills, as well as to make academic results visible. It is concluded that, in their variety, versatility and potential, the appropriation of these resources are the opportunity to contribute to the reduction of other labor differences between genders in this professional field.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".