Cobertura mediática en desapariciones: análisis del caso de Gabriel Cruz y Diana Quer
Bibliographic record
Abstract
En el presente trabajo se realiza un análisis de la cobertura realizada por los medios de comunicación cuando ocurre una desaparición. El objetivo principal de la investigación es detectar cuáles son las negligencias realizadas por los periodistas para aportar una serie de recomendaciones que podrían ayudar a los medios de comunicación ante la inexistencia de un código específico. Para ello, se ha llevado a cabo un estudio cuantitativo de 40 noticias abordadas por distintos medios sobre la desaparición de Gabriel Cruz y Diana Quer; así como un estudio cualitativo de dos entrevistas semiestructuradas y un análisis de documentación agrupando también la cobertura realizada a nivel internacional. Tras ello, se concluye que las desapariciones son sucesos traumáticos altamente mediáticos, donde la complejidad y la necesidad inmediata de informar terminan en una mala praxis de muchos periodistas.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".