Bibliographic record
Abstract
espanolEl presente comienza analizando distintos tipos de sistemas y modelos normativos que amparan el derecho fundamental a la proteccion de datos. Analiza los modelos que presentan la Union Europea, los Estados Unidos de Norteamerica, Canada, para luego irse circunscribiendo a Iberoamerica y America Latina. A continuacion describe integracion y cometidos de la Red Iberoamericana de Proteccion de Datos, enumera los documentos aprobados por esta para comentar especificamente uno de ellos. Para finalizar, detalla la situacion normativa en la materia en la Republica Oriental del Uruguay y concluye describiendo el sistema uruguayo en proteccion de datos personales. EnglishThe following begins analyzing different regimes for data protection, describes the regulation on data protection and habeas data in the European Union, the United States of America, Canada, Iberoamerica and Latin America. It continues explaining the activities of the Red Iberoamericana de Proteccion de Datos (Iberoamerican data protection network), which was established as a result of an initiative put forward by the Agencia Espanola de Proteccion de Datos (Spanish data protection agency), its constitution, purpose, tasks, and the series of documents approved, in particular the set of norms recently approved (2007) on general principles on data protection. On the last part, the document describes the Uruguayan regime on data protection.
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.
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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".