Bibliographic record
Abstract
Low usage statistics for library resources are a big concern for the librarians at the State University of New York (SUNY) Buffalo State, so we were unprepared for the popularity of a new streaming video patron-driven acquisitions (PDA) program. Though it was slow to take off, when it did, usage increased suddenly and dramatically. After depleting the initial budget for the resource, we allocated more funds and then quickly depleted those additional funds. At that point, we changed to a mediated model to help control the costs, but that greatly increased work for our Acquisitions Department and raised collection development questions we had not considered when we began the PDA program. To continue to offer a streaming video PDA program, we reviewed various models and controls before deciding on an approach that we hoped would give users good options, curtail costs, and minimize workloads. This paper will provide a quick summary of our program’s explosive growth, what we did to control costs, the unforeseen consequences, and how we tried to enhance the experience for everyone. We conclude with the current state of streaming video PDA at our library. This paper will provide practical information for small to mid-sized academic libraries that have recently begun or are contemplating streaming video PDA.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".