Bibliographic record
Abstract
La macro-evaluacion de documentos es una teoria y una practica archivistica que permite seleccionar el material de archivo, partiendo de las funciones y relaciones internas y externas del ente creador, llegando solo en la ultima faz del proceso a la revisacion de la documentacion propiamente dicha. Este metodo permite no solamente una vision global de todo el sistema archivistico de un ente determinado sino tambien de entes relacionados entre si, tales como sistemas gubernamentales, empresariales, universitarios, etc. Y sus relaciones reciprocas con la sociedad y los ciudadanos que la componen. Esta resena tiene por objeto presentar la teoria, metodologia y practica de la macro-evaluacion. Los beneficios de la misma tanto en la administracion de archivos corrientes como en los de deposito y archivos historicos, recalcando especialmente la contribucion del sistema a la creacion de la memoria colectiva de una sociedad determinada. Los contenidos comprenden una resena historica de metodologias de evaluacion anteriores a la macro-evaluacion. La teoria del y la nueva interpretacion de la evaluacion documental, poniendo el acento en cuatro ejemplos: Canada, el Reino Unido, los Paises Bajos y Australia
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".