Communicating Collections: Strategies for Informing Library Stakeholders of Collections, Budget, & Management Decisions
Bibliographic record
Abstract
A challenging aspect of the collection management process is effectively communicating with stakeholders about library resources. Communication can range from obtaining patron feedback integral in collection planning to effective messaging elaborating on collection budgets and cancellation decisions. It has also become increasingly necessary to explain the various acquisition models that affect the landscape of library content and use of electronic resources. In this paper, the University of South Florida will present the results of a survey of the approaches used in academic library websites to communicate collection policies along with related considerations, statistics and data, justifications, and factors affecting selection practices. Information about the important elements used to construct a dialog with faculty and administration in order to demonstrate the costs and value of library resources to those in the academic community is included. A case study demonstrating the practical implementation of these communication principles at the Arthur Lakes Library at the Colorado School of Mines will be discussed. The study will show how the Library was able to break free from a cycle of collection stagnation, which was perpetuated by a lack of effective communication. The result was that the Library was able to tell a story with data in order to communicate a message, as well as strengthen their partnerships with faculty regarding collection management.
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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 | 0.077 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.027 | 0.034 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".