Collecting and Selecting: A Tale of Training and Mentorship
Bibliographic record
Abstract
The shifting landscape of collections development and management, in conjunction with changing staffing models and priorities, has required an evolution of selection responsibilities at the University of Toronto.An administratively complex library system with over 40 libraries and three campuses serving over 88,000 students, significant portions of the University of Toronto Libraries collections were historically built by selectors in the centralized Collection Development Department.Over the past decade, the model has evolved from a single individual selecting for all physical and applied sciences to many selectors, and of engineering and computer science disciplines have finally moved to a fully dispersed model where liaisons in the Engineering & Computer Science Library (ECSL) select for their liaison areas.Historically at the larger U of T Libraries, selection and liaison duties have been separate roles, ostensibly to let selectors and liaisons focus on developing the expertise and experience for their specific role.Over time, staffing levels at ECSL and librarian interest have necessitated a shift to a more distributed model for selection.In this paper, the authors will discuss how selection training has evolved over the years to become a robust program that includes ongoing mentorship and support, a new system-wide Collections Community of Practice initiative, and growing selector empowerment and capacity building in e-resource management and assessment through the resource lifecycle.As none of the current ECSL selectors were hired into their positions with selection duties but have had those duties added as the staffing model and requirements of the ECSL has changed, training and mentorship has become an important step in creating and maintaining the high-quality collections on which the University of Toronto prides itself.The paper will also look at the experience of the ECSL librarians taking on selection for their liaison areas and the benefits and challenges of adding on the extra work and responsibility.The drawbacks and rewards of dispersing selection more generally will be discussed, as well as the mentorship and feedback in terms of collections philosophies as more experienced selectors train and mentor their colleagues new to this role.
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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 | 0.060 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.026 | 0.017 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 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".