The Computerization Of Material Culture Catalogues: Objects and Infrastructure in the Smithsonian Institution's Department of Anthropology
Bibliographic record
Abstract
Abstract The history of museum collections is also the history of the management of information about these collections. Today, increased access to large amounts of robust collections data requires that information be curated so that is useful for communities and individuals who wish to access it. This has caused scholars and communities to question modes of ordering that do not necessarily map onto their own local and personal understandings of the world. In light of the major pragmatic and intellectual affordances stimulated by information technologies, the inner workings of these systems are often made invisible and act as infrastructures rather than singular or simple tools. By providing a historical account of how information about anthropological museum collections was computerized in the 1970s at the Smithsonian Institution's National Museum of Natural History (NMNH), I consider the museum catalogue as a socio‐technical information infrastructure. From that perspective, I argue that the knowledge produced by modes of inscription such as catalogues is generated by relationships of individuals and technologies. A detailed and critical history of catalogues must take into account these historical socio‐technical infrastructures.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".