Bibliographic record
Abstract
Este ensayo explota la literatura sobre clasificacion biologica como medio para iluminar ciertos aspectos de la clasificacion en gestion de documentos y en Archivistica. Al clasificar items que son resultado de procesos causales complejos y forman una unidad organica, los biologos y los gestores de documentos estan sujetos a restricciones similares: sus clasificaciones deben reflejar los procesos causales subyacentes, revelando el “vinculo oculto de conexion”: la genealogia en biologia y el vinculo archivistico en Archivistica. El autor argumenta que las discusiones de la clasificacion biologica tambien arrojan luz sobre las limitaciones de una clasificacion puramente funcional que descarta los procesos de negocio de nivel inferior.CLASIFICACION DE DOCUMENTOS / CLASIFICACION BIOLOGICA / CLASIFICACION GENETICA / TAXONOMIA / VINCULO ARCHIVISTICO / CLASIFICACION FUNCIONALThis paper draws on the literature of biological classification as a means to illuminate certain aspects of classification in records management and archival science. In classifying items that are the result of complex causal processes and form an organic unit, biologists and records managers are subject to similar constraints: their classifications must reflect the underlying causal processes, uncovering the “hidden bond of connection:”genealogy in biology and the archival bond in archival science. The author argues that discussions of biological classification also shed light on the limitations of a purely functional classification that disregards lower-level business processes.RECORDS CLASSIFICATION / BIOLOGICAL CLASSIFICATION / GENETIC CLASSIFICATION / TAXONOMY / ARCHIVAL BOND / FUNCTIONAL CLASSIFICATION
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".