Current trends in collection development practices and policies
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a snapshot of some major collections-related trends and issues in current academic libraries today. These include using collection development policies; demand-driven acquisition (DDA) models; big deals; using the collections budget; rationalizing legacy print collections; stewarding local digital collections; and demonstrating value. Design/methodology/approach A web survey was developed and sent to 20 academic librarians via e-mail during the summer of 2016, along with a statement on the purpose of the study. Findings The findings are as follows: the collections budget is used to fund many costs other than content (such as memberships and MARC records); most libraries are experimenting with DDA in one form or another; most libraries financially support open access investments; most libraries participate in at least one collaborative print rationalization project; and libraries have diverse methods of demonstrating value to their institutions. Research limitations/implications This was a very selective survey of North American academic libraries. Therefore, these findings are not necessarily valid on a broader scale. Practical implications Within the limitations above, the results provide librarians and others with an overview of current practices and trends related to key issues affecting collection development and management in North America. Originality/value These results are quite current and will enable academic librarians engaged in collection development and management to compare their current policies and practices with what is presented here. The results provide a current snapshot of the ways in which selected libraries are coping with transformative challenges and a rapidly changing environment.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.021 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".