Adaptación a entornos cambiantes
Bibliographic record
Abstract
Las autoridades responsables de los archivos se han enfrentado internacionalmente a desafíos para mantener su relevancia en los entornos predominantemente digitales. En este trabajo se reseñan iniciativas innovadoras de las autoridades de archivos que van desde la valoración de las culturas de informaci.ó organizacional hasta las actividades desarrolladas durante la pandemia de COVID-19. Los nuevos enfoques para la formación y la educación de los archiveros son esenciales paragarantizar la continuidad de la pertinencia y del éxito de la misión del archivo. Internationally, government archival authorities face challenges in ensuring their relevance in predominantly digital environments. this paper reports on innovativ initiatives involving archival authorities, ranging from assessment of organisational information cultures to activities during the COVID-19 pandemic. In order to ensure continued relevance and the success of the archival mission, new approaches to training and education for archivists are essential.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 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".