Una metodología para modelar fenómenos culturales latentes: interés público en la red Razón Pública
Bibliographic record
Abstract
Este artículo amplía el horizonte de análisis en el campo de las redes culturales, al proponer una metodología basada en datos para modelar fenómenos culturales latentes.A modo de ilustración, se aplicó el diseño metodológico en un estudio sobre los temas de interés público en la revista Razón Pública.Primero, se revisó el término interés público y se justicó la pertinencia de concebir este tipo de temas como fenómenos culturales latentes en la red de la publicación.Segundo, se presentó el periodismo digital en Colombia como el dominio cultural de la red.Para la elaboración del modelo, se propuso una estrategia para delimitar, obtener y estructurar los nodos de los actores y objetos culturales.Finalmente, se explicó el uso de la técnica Asignación Latente de Dirichlet (LDA) para modelar los temas de la revista y se validó con una encuesta que conrmó los temas de interés público, según algunos actores de la red.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".