From Big Ideas to Real Talk: A Front-line Perspective on New Collections Roles in Times of Organizational Restructuring
Bibliographic record
Abstract
Academic libraries across North America are restructuring to meet user needs in an e-preferred environment, resulting in major changes to traditional collection development roles and workflows. Responsibility for collection work is increasingly assigned to functional librarians dedicated to collection development activities across a broad range of subject areas, often serving an entire faculty or college. This paper discusses the history, process, and outcomes of the transition to functional collection development roles at two mid-sized universities. Both Carleton University and the University of Guelph support a wide range of undergraduate and graduate research needs from a single central library, but have implemented a different type of organizational design and are at different stages in the restructuring process. One year into their new functional roles, Carleton’s librarians are preparing to assess the state of change around collection development in their organization, and identify next steps for the restructuring process. By contrast, the University of Guelph has worked with a functional team model for ten years, and is undertaking a 10-year review to assess whether the original goals of the reorganization were met. How does collections work compare under a functional team model, compared to a traditional liaison model? Both perspectives offer strategies for consultation and change management that may be helpful to other institutions restructuring their collection development activities.
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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 | 0.037 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.035 | 0.075 |
| Scholarly communication | 0.048 | 0.047 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 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".