Arriaga eszenatokira-El Arriaga, a escena: La danza del hierro
Bibliographic record
Abstract
¿ H AY una trama c o n c reta en B u r n i a?-Nadie habla, todo se hace mediante el baile.He tomado diferentes partes de la historia de Bizkaia relacionadas con el hierro, diez pasajes que van desde la segunda guerra carlista hasta la guerra civil.-¿Es el hierro el p rotagonista absolut o ?-El hierro y sobre todo la gente que trabajó de manera directa o indirecta con el hierro, por el hierro, la que ganó dinero con el hierro, la que lo perdió… toda esa gente que lo puso como eje de su vida.-¿Es su primera vez en la danza?-Sí, jamás he sido un gran aficionado a la danza ni había tratado con nadie que hiciese coreografías.El director, Edu Muruamendiaraz estaba muy emocionado con el proyecto y yo dije "¿por qué no?".Como dice Fernando Velázquez, "somos unos amateurs venidos a más.En todo lo que hacemos somos amateurs".Además las danzas son espectaculares, y con la música de Fernando seguramente mejorarán y me
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".