Patron-Driven Acquisition – Working Collaboratively in a Consortial Environment: An Interview with Greg Doyle
Bibliographic record
Abstract
Patron-driven acquisition models for electronic and print books have become extremely popular in the past two years and in most cases this service has been implemented at many individual libraries. One unique collaborative model of patron-driven acquisition was created by the Orbis Cascade Alliance through a partnership with Ebook Library (EBL) and Yankee Book Peddler (YBP). This unique project is an example of libraries, consortia, and vendors working together to develop new business models during times of financial constraint, where libraries and consortia are exploring various “just-in-time” acquisition models. Collaborative Librarianship spoke with Greg Doyle about the project at Orbis Cascade.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.028 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".