Introducción, objetivos y otros aspectos a incluir en un Artículo Científico
Bibliographic record
Abstract
Con este articulo retomamos la serie de trabajos que tienen como objetivo general ayudar a presentar con mayor rigor cientifico las conclusiones de nuestras investigaciones. Los articulos son documentos, Nuria Amat (1994) los define como «todo acontecimiento fijado materialmente sobre un soporte que puede ser utilizado para consulta, estudio o trabajo». De esta forma esta completo, como dice Price (1978), el acto de creacion en la investigacion cientifica, con la publicacion se comunican los acontecimientos nuevos y estos pueden ser sometidos a evaluacion y tambien al asentimiento de la comunidad cientifica. La mayor parte de las publicaciones cientificas de nuestra disciplina siguen las recomendaciones del Grupo de Vancouve, que surge en 1978, cuando un pequeno grupo de editores de revistas medicas se reune en Vancouver (Canada) con el objetivo de establecer unas directrices respecto al formato de los manuscritos enviados a sus revistas. Anos mas tarde se amplia y evoluciona hasta convertirse en el Comite Internacional de Editores de Revistas Medicas (International Committee of Medical Journal Editors, ICMJE) que se reune anualmente. El Comite ha elaborado cinco ediciones de los «Requisitos de uniformidad para manuscritos presentados para publicaciones en revistas biomedicas», por su interes las publicadas en 1997 van a ser nuestra guia.
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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".