Bibliographic record
Abstract
La especial naturaleza del ajedrez hace que su interrelación con los derechos de autor resulte problemática e incierta. El presente trabajo aborda esta cuestión a través del análisis de las tres manifestaciones del ajedrez por antonomasia; la partida, las posiciones y movimientos destacados, y los estudios y composiciones, y la posibilidad de que éstas queden amparadas bajo el umbral de protección dispensado por los derechos de autor. Para ello, se lleva a cabo un análisis de los presupuestos inherentes a los derechos de autor a través del estudio de la legislación y jurisprudencia aplicables y la concurrencia de los mismos en las mentadas manifestaciones del ajedrez.Due to the special nature of chess, its interrelation with author`s rights is problematic and uncertain. The present work approaches this question through the analysis of the three most important chess expressions; the game, remarkable positions and moves, and the studies and compositions, and, the possibility of them being covered by the protection offered by author`s rights. In order to do so, an analysis of the inherent requisites of author`s rights was carried out through the study of the applicable legislation and case law and the concurrency of them in the abovementioned chess expressions.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".