Bibliographic record
Abstract
En tanto que historiadores del arte que hemos participado ?ltimamente de ma nera activa en conferencias en las que se buscaba reinventar o red?finir el barroco, nos ha impresionado el continuo sentimiento de redescubrimiento e in venci?n que rodea al t?rmino. El barroco tambi?n ejerce en la actualidad una considerable atracci?n entre acad?micos de diferentes disciplinas y, como histo riadores del arte, nos llama la atenci?n la aparente libertad con la que el concepto es usado en otros campos. Estimulados por este auge de actividad alrededor del barroco, hemos dise?ado un peque?o cuestionario para tomar el pulso del t?r mino mismo. En las p?ginas que siguen se reproducen las respuestas de quince historiadores del arte, cinco cr?ticos literarios y ocho historiadores de diferentes ?reas unidos por trabajar todos de una manera u otra sobre el barroco y a quie nes se les dirigieron seis preguntas indic?ndoseles que respondieran en lo posible de manera breve (e inteligente). Se les dijo a los participantes que no quer?amos cargarles de trabajo y que el survey no deb?a de durar m?s de lo que se tarda en comer un delicioso pastel. En la presentaci?n de los resultados que sigue a continuaci?n los autores ofrecen una breve discusi?n de las diferencias generacio nales, disciplinarias y geogr?ficas que han determinado dicho cuestionario.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.038 | 0.014 |
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".