Análisis del fenómeno de BookTube en España
Bibliographic record
Abstract
YouTube está repleto de diversas comunidades, de grandes grupos de gente que comparten una misma afición y realizan vídeos relacionados con ella. Amantes de los videojuegos, gurús de moda y belleza, músicos, cocineros… incluso los apasionados de la lectura se abren paso en la gran plataforma de vídeos, consolidándose en una pequeña comunidad llamada BookTube. El trabajo en cuestión pretende adentrar al lector en este mundo aún poco conocido, concretamente, en el papel que juega en España, tanto en el mercado editorial como en los jóvenes lectores. Para ello, en las siguiente páginas se exponen los resultados de una investigación que parte del núcleo de la propia comunidad, con entrevistas personales a diversas editoriales y booktubers, así como del análisis profundo de los canales de estos últimos. Para terminar, la investigación se extiende con la toma como referencia de algunos de los pocos artículos académicos escritos sobre BookTube hasta el momento.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".