Bibliographic record
Abstract
Acquisition of unique digital material is an ongoing challenge for Special Collections units—often unique digital material comes with little or no metadata associated with the digital objects. Using two ongoing projects at the University of Oregon Libraries as case studies, this paper explores strategies archivists and librarians can use to increase descriptive metadata coming in with unique born-digital collections. Library staff involved with the Latino Roots and University of Oregon Veterans Oral History projects work with the content creators, in this case faculty and students, to build collections with rich descriptive information that is relevant both to librarians and to the communities being documented.
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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 | 0.060 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.018 | 0.029 |
| Scholarly communication | 0.038 | 0.052 |
| Open science | 0.009 | 0.044 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".