Análisis bibliométrico sobre el alumnado con altas capacidades (2010- 2018)
Bibliographic record
Abstract
El análisis bibliométrico realizado tiene como objetivo conocer y analizar una muestra de artículos, revistas, autores, para conocer la evolución en cuanto a la publicación sobre el alumnado con altas capacidades, en el periodo temporal que transcurre de 2010 a 2018. Para ello, se llevó a cabo una búsqueda a través de la base de datos Web of Science y tras pasar varios filtros en función de unos criterios de exclusión establecidos previamente se seleccionó una muestra total de 198 artículos. En cuanto a los resultados obtenidos, el año de mayor publicación fue 2016 con 41 artículos, la revista especializada con más publicaciones es Gifted Child Quaterly con 30 artículos publicados, por lo referente a las producciones por autor destacan los transeúntes con un 89%. Una vez realizado el estudio se concluye sabiendo que en la actualidad se está dando un descenso en cuanto a la publicación del tópico se refiere, las revistas que mayor publicación tienen sobre las altas capacidades son revistas especializadas en el tema, prácticamente no existen autores dedicados exclusivamente al tópico, de la misma manera que estos prefieren trabajar de manera individual o en parejas, ya que conforme aumenta el número de autores, desciende el número de publicaciones.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.060 | 0.084 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".