¿De qué hablan los medios cuando hablan de propiedad intelectual? Un análisis comprada de contenido en la prensa de calidad
Bibliographic record
Abstract
This paper seeks to present the principal aspects and rights (moral, financial- operational or of related nature) dealt with by the leading quality media of a representtative sample of the two main legal traditions: common law (with the copyright system) and civil law (with the authors rights system). A content analysis is conducted on the news published by the aforementioned media in the United States (The New York Times, The Washington Post); Canada (The Globe and Mail, La Presse); France (Le Monde, Le Figaro); Spain (El Pais, El Mundo); Italy (La Repubblica, Corriere della Sera); and the United Kingdom (The Guardian, The Times), in the opening months of 2014, when several reforms were made in intellectual property, both within these nations and in supranational structures (European Union, WIPO-OMPI).
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".