Bibliographic record
Abstract
ResumenEl artículo analiza el rol de los peritos en casos de plagio.Los expertos técnicos, convocados por el juez o las partes, deben evaluar las semejanzas entre las obras en conflicto, cualquiera sea el género del que se trate.El experto requiere competencias técnicas para fundamentar un dictamen razonado que aplica las reglas técnicas y científicas de la disciplina correspondiente.Asimismo, analiza el proceso creativo, distinguiendo los elementos comunes que no resultan apropiables de aquellas expresiones creativas de una obra que son copiadas en otra, y debe establecer si esas semejanzas obedecen a creaciones independientes o si se trata de apropiación de lo ajeno, sin que se pueda justificar desde la casualidad o el accidente.Ante esto, se proponen pautas para aplicar el test de similitud sustancial, de modo que el juez pueda contar con la información suficiente para emitir un juicio absolutorio o condenatorio.Se sugiere la utilización de recursos técnicos específicos, considerando cada categoría de obras.A su vez, se indican qué elementos serán relevantes para poder impugnar una pericia o para establecer su validez, considerando que el juez no puede apartarse arbitrariamente de un dictamen sostenido adecuadamente en las reglas del arte y los fundamentos teórico-prácticos de la disciplina de la que se trate.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".