Transforming Acquisitions and Collection Services : Perspectives on Collaboration Within and Across Libraries
Bibliographic record
Abstract
This book explores ways in which libraries can reach new levels of service, quality, and efficiency while minimizing cost by collaborating in acquisitions. In consortial acquisitions, a number of libraries work together, usually in an existing library consortia, to leverage size to support acquisitions in each individual library. In cross-functional acquisitions, acquisitions collaborates to support other library functions. For the library acquisitions or technical services manager, or the library director, awareness of different options for effective consortial and cross-functional acquisitions allows for the optimization of staff and resources to reach goals. This work presents those options in the form of case studies, as well as useful analysis of the benefits and challenges of each.\n\nBy supporting each other’s acquisitions services in a consortium, libraries leverage size to get better prices, and share systems and expertise to maximize resources while minimizing costs. Within libraries, the library acquisitions function can be combined with other library functions in a unit with more than one purpose, or acquisitions can develop a close working relationship with another unit to support their work. This book surveys practice at different libraries, and at different library consortia, and presents a detailed description and analysis of a variety of practices for how acquisitions units support each other within a consortium, and how they work with other library units, specifically collection management, cataloging, interlibrary loan, and the digital repository, in the form of case studies. A final sections of the book covers fundamentals of collaboration.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.022 | 0.031 |
| Scholarly communication | 0.041 | 0.035 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".