By Popular Demand: Building a Consortial Demand‐Driven Program
Bibliographic record
Abstract
The Orbis Cascade Alliance set out to create an e‐book program for its 36 member libraries. Unlike the single-library patron‐driven acquisition programs that we have seen in the past, this ambitious pilot needed to take into account the different discovery options and workflow requirements of 36 libraries and their varying size and technical capabilities. We will discuss the ideal makeup of an implementation team for a program of this size, how to assess the technical hurdles and what training must be provided, how to work with vendors effectively in this setting, and how to evaluate the success of a patron‐driven program, both during the program and afterward. We will include lessons learned that are applicable both to individual libraries considering patron‐driven programs and to consortia looking to provide a similar service to their libraries.
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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 | 0.030 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".