Calidad y equidad en el futuro de la educación: El problema de la igualdad de oportunidades sigue siendo un problema
Bibliographic record
Abstract
The educational models applied, like the cultural demands of the job and career world, bring about unfair situations whose solution is society’s moral duty to deal with. It is not a question of who did not go to school or abandoned it, but of those who go and do not receive the attention the others did. This is an unfair situation as much as that of the illiterate. However, this is more immoral because they do not receive equal opportunities when going and trusting school’s responsibility. It is necessary to update new practices that permit the creation of co-operative dynamics, which guarantee a chain of the value of knowledge, of its construction and its management through integrated and hypertextual systems. I only intend to supply educational compensation ideas that act on what has been produced and is being produced by the models, contributing to avoid new types of exclusions which attempt against educational quality and equity.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.053 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".