Oral History, Donor Engagement, and the Cocreation of Knowledge in an Academic Archives
Bibliographic record
Abstract
This article examines attempts at the Southwest Collection at Texas Tech University’s Southwest Collection/Special Collections Library (SWC/ SCL), in Lubbock, Texas, to integrate its oral history program into collection acquisition, arrangement, description, and discovery processes. Beginning with the creation of a staff position dedicated to acquisition, and continuing through an evolution of job duties resulting from COVID-19, the SWC’s oral historians now not only facilitate collection acquisition through extensive relationship building but also engage donors during arrangement and description. Such reconceptions have led to new processes and workflows, wherein oral history has become an endeavour of collaborative knowledge creation and an enabler of a more democratic archives.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.029 | 0.038 |
| Scholarly communication | 0.028 | 0.011 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".