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.
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 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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".