THE FUTURE OF COLLECTIONS: AN APPROACH TO COLLECTIONS MANAGEMENT TRAINING FOR DEVELOPING COUNTRIES
Bibliographic record
Abstract
Natural history collections in Latin America are growing, and the rates of collection use are increasing. In response to the need for professional collections care train- ing in Latin America, we developed a comprehensive workshop to provide on-site training. The workshop uses a combination of lectures, readings, and hands-on activities to teach participants how to better manage collections and identify archivally sound materials. The workshop is structured around a conceptual model for teaching the theoretical bases of collections management that integrates preventive conservation with concepts of order and collection growth, and includes the history of collections, emphasis on the quality of the storage environment, and collection assessment. The workshops have identified several new areas for collections care research. The model can be successfully applied to other devel- oping regions outside of Latin America. Resumen.—Las colecciones de historia natural de America Latina estan creciendo, y la tasa de su uso tambien esta creciendo. Por lo tanto la necesidad de oportunidades para capacitacion profesional del cuidado de las colecciones en America Latina, desarrollamos un taller completo para proveer capacitacion en sitio. El taller es una combinacion de pre- sentaciones, lecturas, y actividades para ensenar a los participantes como manejar mejor las colecciones e identificar materiales archivables. El taller esta estructurado sobre un modelo conceptual para ensenar las bases teoreticas del manejo de colecciones que integra conser- vacion preventiva con los conceptos de orden y crecimiento de las colecciones, e incluye la historia de las colecciones, un enfasis en la cualidad del ambiente de almacenamiento, y la evaluacion de las colecciones. Los talleres han identificado algunas areas nuevas para la investigacion del cuidado de las colecciones. Nuestro modelo puede ser aplicado con e en las regiones en desarrollo fuera de America Latina.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.016 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| 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 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".