Enclaves de lirismo en el ensayismo "vaginal" de Ángel Antonio Herrera. Calas en "Mujeres, mujeres"
Bibliographic record
Abstract
Ángel Antonio Herrera is a Spanish journalist and poet who reached his peak of public notoriety in the nineties, mainly appearing in the yellow press as a commentator, interviewer and chronicler. However, his purely literary trajectory, mainly focused on a poetic creation of great lyrical strength, has been buried by his journalistic trajectory. However, both have been related by the lyrical impetus of Herrera, who has always transferred findings more typical of the poetic orb to his columns and journalistic essays. As he himself has acknowledged, he learned from his teacher Francisco Umbral. An example of this is his book Mujeres, mujeres (1992). In this book he makes a series of portraits of different models, actresses or writers, interspersed with a large number of lyrical resources that contribute to give a greater aesthetic-literary value to the whole.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".