El complejo asunto de la educación superior: aristas, enfoques y desafíos
Bibliographic record
Abstract
La educación constituye el pilar fundamental del desarrollo de cualquier sociedad y hoy adquiere una especial connotación debido a la imperiosa necesidad de generar innovaciones que produzcan un aporte real a la sociedad, lejos de modelos superfluos y de tendencias que aparecen como modas de un día pero que no proveen la solidez necesaria para garantizar proyectos educativos sostenibles. En este orden de ideas, la educación superior es la llamada a tomar un protagonismo en la formación de profesionales que comprendan la sociedad, el entorno y el mundo de hoy, pero que a través de su formación integral apalanquen la frontera del conocimiento, así como la frontera de la construcción social sostenible. Sin embargo, este ámbito de la educación superior no está exento de desafíos y complejidades, que requieren un análisis profundo y soluciones innovadoras. Es en este contexto que surge la obra “EL COMPLEJO ASUNTO DE LA EDUCACIÓN SUPERIOR: ARISTAS, ENFOQUES Y DESAFÍOS”, como una valiosa contribución para comprender y abordar algunos de los asuntos que caracterizan a este campo.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".