Specimens as Records: Scientific Practice and Recordkeeping in Natural History Research
Bibliographic record
Abstract
For the past two decades, scholars in archival science have begun to question traditional assumptions about the nature of the record. Drawing on theories from fields such as sociology, organization theory, and science studies, and on their own ethnographic studies, they propose more inclusive definitions and widening the contexts of analysis of record making and recordkeeping. This paper continues this critical consideration of the concept of record by examining the nature of nonprototypical records in the scientific world. The paper focuses on the system of specimens and field notes established by biologist Joseph Grinnell at the Museum of Vertebrate Zoology (University of California, Berkeley) as a means of examining several aspects of the nature of the scientific record: materiality, representation, and the triad evidence/memory/accountability. Focusing on the creation and management of these scientific records, the paper argues that further analyses of scientific record making and recordkeeping are bound to benefit both scientific work, which depends more and more on databases and archives, as well as archival science, which is becoming more relevant beyond its traditional realm of the legal/business/administrative world.
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.
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 | 0.046 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.010 | 0.083 |
| Scholarly communication | 0.026 | 0.034 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".