Drones recreativos, responsabilidad civil y protección de datos (Tras la reforma de 2017)
Bibliographic record
Abstract
espanolLos dos grandes retos que los drones recreativos plantean en la actualidad, como consecuencia de su reciente proliferacion entre los aficionados, son asegurar la seguridad del vuelo y prevenir vulneraciones de los derechos fundamentales. Tras el examen del Real Decreto 1036/2017 de 15 de diciembre –desde una perspectiva de derecho comparado con respecto a Canada y Estados Unidos de America– y sobre la base de un analisis de la responsabilidad civil del propietario y del fabricante por los danos causados, se concluye que el marco legal actual resulta insuficiente para evitar futuros incumplimientos de la normativa reguladora de la proteccion de datos. Es necesaria una mayor tarea de informacion entre los nuevos usuarios de esta tecnologia, asi como de una mayor implicacion del legislador y de los fabricantes.Los dos grandes retos que los drones recreativos plantean en la actualidad, como consecuencia de su reciente proliferacion entre los aficionados, son asegurar la seguridad del vuelo y prevenir vulneraciones de los derechos fundamentales. Tras el examen del Real Decreto 1036/2017 de 15 de diciembre –desde una perspectiva de derecho comparado con respecto a Canada y Estados Unidos de America– y sobre la base de un analisis de la responsabilidad civil del propietario y del fabricante por los danos causados, se concluye que el marco legal actual resulta insuficiente para evitar futuros incumplimientos de la normativa reguladora de la proteccion de datos. Es necesaria una mayor tarea de informacion entre los nuevos usuarios de esta tecnologia, asi como de una mayor implicacion del legislador y de los fabricantes. EnglishThe two great challenges raised by recreational drones presently, as a consequence of their recent proliferation among hobbyists, are ensuring flight safety and preventing violations of fundamental human rights. Following the review of the recently passed Royal Decree 1036/2017 of 15 December –from a comparative perspective with respect to Canada and United States of America– and on the basis of a thorough analysis of civil liability of the owner and the manufacturer for harm caused, it is concluded that the current legal framework is inadequate to avert violations of data protection regulation. A greater effort to provide information among the new users of this technology is required, as well as further involvement of the legislator and manufacturers.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".