El bolso de Ana Karenina o la importancia de los inventarios de archivos auto/biográficos
Bibliographic record
Abstract
Una de las l?neas m?s estimulantes en el estudio de la escritura auto/biogr?fica es la de recuperar archivos y escritos personales que suministran valiosa informa ci?n multidisciplinar. Es una tarea que requiere una firme pol?tica cultural: en muchos casos esos archivos han carecido, en el pasado, de un apoyo institucional, de una infraestructura adecuada que permita preservarlos de su posible destruc ci?n o p?rdida. La necesidad de inventariar los archivos autobiogr?ficos es una tarea pendiente en el ?mbito hisp?nico, a pesar de algunas iniciativas que se est?n llevando a cabo. La autora reflexiona sobre la emprendida por la Unidad de Estudios Biogr?ficos, en ese aspecto, y los fondos reunidos en los diez a?os de funcionamiento (1994-2004).
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 | 0.030 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".