SEO académico aplicado a revistas en Acceso Abierto
Bibliographic record
Abstract
El objetivo fundamental de las publicaciones científicas es la divulgación del conocimiento, como dice el lema de Redalyc, “la ciencia que no se ve, no existe”. De esta manera, resulta clave conocer las principales acciones o técnicas para aumentar la visibilidad de las publicaciones y así aumentar la posibilidad de citación. De estos dos objetivos nace el SEO Académico o ASEO (por sus siglas en inglés, Academic SEO). Inicialmente el SEO (Seach Engine Optimization) consiste en las diferentes técnicas para posicionar páginas web en los primeros resultados de búsqueda de los buscadores. Por lo tanto, el SEO Académico consiste en aplicar técnicas a nuestras publicaciones que permitan mejorar su visibilidad en los buscadores académicos y así aumentar la posibilidad de ser citados (Codina, 2016). En este trabajo se exponen los resultados obtenidos con la aplicación de algunas de las técnicas de SEO Académico en revistas de acceso abierto de Costa Rica.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".