Begin at the Beginning: Revamping Collection Development Workflows
Bibliographic record
Abstract
“Begin at the beginning,” the King said, very gravely, “and go on till you come to the end: then stop.” This paper describes how two librarians newer to the University of Tennessee Libraries refreshed collection development workflows at the Libraries after a reorganization. This reorganization distributed tasks across departments in a different manner due to the new departmental configurations. In this new matrix environment, more communication was required to achieve desired outcomes, but more buy-in was also needed from constituents such as the subject librarians. This paper describes how a new Collections Committee was formed to make decisions on high-dollar resources; what information was added to the traditional request form to facilitate the committee’s decisions; what information was asked of vendors at the point of trial or initial interest; and how this fed into a new collection development policy. By revamping the workflows to ask for more information up front, the presenters were able to help the new Collections Committee obtain all the information needed for decision-making at the point of decision. The authors share insights into how organizational changes can be used as an opportunity to instigate workflow changes that help libraries acquire resources more nimbly and flexibly.
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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 | 0.095 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.028 | 0.023 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".