Bibliographic record
Abstract
This paper examines analog records, ledger book pages and catalog cards, at the Smithsonian Institution (SI) National Museum of Natural History within the anthropology collections, examining how these documents advance an information infrastructure categorizing the Pacific Northwest materials making up accession 000051. Within this case study and textual analysis, I found that using a nation-building lens elucidated knowledge organization (KO) logics at play with these records. My critical reading outlines the ways these records firmly place Indigenous materials and belongings under SI and broader Euro-American knowledge systems via language use and place names, rhetorically furthering land dispossession and functioning to realize the US as a nation. This paper positions KO and associated rhetoric and language use as a tool of nation building, and museum records as a process, a genre of writing that have been honed, remade, and reinscribed over time within institutions toward political and national ends. I end this paper describing ongoing projects supporting and furthering Indigenous sovereignty via KO work in relation to cultural heritage collections, illustrating responses to the nation-building rhetoric within the SI records I analyze.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.020 | 0.047 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".