Mapa de los cibermedios de España en 2018: análisis cuantitativo= Map of digital news media at Spain in 2018: quantitative analysis
Bibliographic record
Abstract
Introduction: On the verge of reaching their first quarter of a century, the digital news media in Spain \ncontinue with their unstoppable growth. This research presents an updated map, as of March 2018, of \ndigital news media in Spain. Methodology: It identifies and analyses a sample including 3,431 digital \nmedia, identified thanks to a thorough review of directories of the media industry, as well as the use \nof several advanced search strategies. This dataset provides the largest sample of Spanish digital \npublications so far. Results: Among the media studied, aspects such as the current status of these \npublications (either active or inactive), number and type of platforms where they are published, news \ntopic, scope, territorial distribution, publishing languages, ownership and business model are \nstudied. Discussion and Conclusions: The result is the most updated and comprehensive, although \nnot exhaustive, map of digital news media in Spain, which portrays these publications as a highly \nconsolidated and expanding medium.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.022 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".