Propuesta de indicadores para evaluar las competencias de alfabetización mediática en las administraciones públicas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it