Usar, enseñar, aprender lenguas en la diversidad, globalización y movilidad: Perspectivas conceptuales y metodológicas
Bibliographic record
Abstract
Este libro contiene una propositiva vertiente de conocimientos y reflexiones acerca de la complejidad multidimensional de los multilingüismos contemporáneos en la operación e infraestructura curricular de la educación escolar y de la enseñanza y aprendizaje de lenguas en cualquier comunidad educativa actual. Los autores exploran el potencial y el significado de los caminos que ha escogido la humanidad para reconocer y dignificar la diversidad cultural, étnica, cognitiva y lingüística, mediante objetivos y metodologías que se sustentan en concepciones democráticas, plurales e integrativas como bases de un nuevo desarrollo humano. Héctor Muñoz Cruz, Coordinador del libro. Al respecto, la Dra. Christiane Paponnet-Cantat, de University of New Brunswick, Fredericton, Canadá, ha expresado: "El marco de investigación que se plantea en este libro incluye dos aspiraciones críticas: reorganizar el ámbito educativo lingüístico y descentralizarlo para concientizar las sociedades al multiculturalismo del mundo actual. Los trabajos aquí reunidos, a través de sus diversas disciplinas, proponen conceptos claros y herramientas precisas para alcanzar estas aspiraciones. Todas las contribuciones tienen sus relevancias e importancias y constituyen un gran aporte a las preocupaciones actuales de los sistemas educativos lingüísticos.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".