Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it