Bibliographic record
Abstract
espanolUn extenso y acabado repaso acerca de lo que el autor, dentro de una linea de investigacion muy nuestra y latinoamericana, denomina la educomunicacion o educacion en medios, pero esta vez en el campo del cine ya que, nos recuerda: educar en cine es educar en medios; es decir, aprender a pensar el proceso de produccion y significacion de mensajes y discursos cinematograficos con el proposito de relacionarlos y/o ponderarlos, de manera critica y constructiva, con la vida cotidiana. Finalmente nos ofrece el estado del arte de la educacion en cine a partir de un conjunto de paises: Union Europea, Reino Unido, Francia, Espana, Italia, Holanda, Belgica, Canada, Finlandia, Mexico, Brasil, Colombia, Chile, Cuba y Venezuela. EnglishAn extensive and well thorough review of what the author, in a research line very ours and Latin American, called the educommunication or media education, but this time in the field of cinema, since he reminds us: education in film is to educate media; that is to learn to think the production process and significance of messages and cinematic discourses in order to relate and / or weight them, critically and constructively with daily life. Finally, he offers a state of the art about film education from a group of countries: EU, UK, France, Spain, Italy, Netherlands, Belgium, Canada, Finland, Mexico, Brazil, Colombia, Chile, Cuba and Venezuela.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.011 |
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".