Brechas de g?nero en las competencias tecnol?gicas relacionadas con el aprendizaje universitario : Incluir el sexo y el g?nero como variables en la investigaci?n cient?fica
Bibliographic record
Abstract
Durante el per?odo de confinamiento global que comenz? en el primer trimestre de 2020, se form? un equipo de investigaci?n que combina inform?tica y antropolog?a para comprender la realidad de los estudiantes que de repente cambiaron su forma de recibir clases de forma presencial a virtual. La investigaci?n se llev? a cabo durante 9 meses. Los resultados mostraron que, en comparaci?n con las mujeres, el manejo de las diferentes tecnolog?as por parte de los hombres durante las clases virtuales debido a COVID-19 fue mayor. Las mujeres tienen m?s desaf?os y su dominio de las tecnolog?as se mantiene en niveles m?s bajos. Esto indica que existe una diferencia en el manejo de las tecnolog?as si tenemos en cuenta el sexo de la poblaci?n consultada. During the period of global lock down that began in the first quarter of 2020, a research team combining computer science and anthropology was formed to understand the reality of students who suddenly changed their way of receiving classes from face-to-face to virtual way. The research was carried out for 9 months. The results showed that compared to women, the management of different technologies by men during virtual classes due to COVID-19 was higher. Women had more challenges and their mastery of technologies remained at lower levels. This indicates that there is a difference in the management of the technologies if we take into consideration the sex of the population consulted
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 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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".