Propuesta de indicadores para evaluar las competencias de alfabetización mediática en las administraciones públicas
Bibliographic record
Abstract
A framework of indicators and a media competence self-assessment test for public administrations is proposed, in a field where few evaluation methods have been implemented up until now. The study is based on media literacy (ML) indicators divided in five general criteria defined by Pérez-Tornero and Celot (2009). These are: availability of media, ML context, use, critical understanding, and communication. The initial frameworks were compared with those used in in highly-regarded international media assessment systems. The article presents a test based on the qualitative comparison of international evaluation methods, as well as through in-depth interviews. The proposed tool is applied and refined through a research process that combines quantitative and qualitative methods. The study concludes that competences can be assessed by means of a questionnaire, but also finds that there are others, especially those related to the critical reading of information, that present greater complexity and that demand various types of tests. The application of the survey allows us to obtain useful recommendations for the development and improvement of ML indicators within public administrations.
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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 | 0.032 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".