Moving from In Person to Online: Effects on Staffing in a Large Academic Library System
Bibliographic record
Abstract
The University of Manitoba (U of M) is a research-intensive medical and doctoral-level university and a member of the U15 Group of Canadian Research Universities, which includes fifteen of Canada’s top research universities. In support of the U of M’s strategic priorities, distributed campuses, and over 100 programs, the University of Manitoba Libraries (UML) comprises eleven libraries that are distributed over two campuses within the city of Winnipeg. While most of the libraries’ general operations, physical collections, and staff are located at the Fort Garry campus, the Health Sciences library is housed at the downtown medical campus. In 2015–2016, the UML reported a staffing compliment of 180 librarians and support staff. Organizational changes were instituted in June 2016 that affected many areas within the libraries, and they were felt acutely in public services where support staff positions were reduced. Traffic, circulation, and reference trends at the UML closely mirrored those reported widely within academic libraries: services and resources are increasingly moving toward online environments, and after several years of consistent decreases in face-to-face informational and circulation transactions, frontline service staff in academic libraries are being reduced and redeployed. A sweeping reorganization of the UML’s public services staffing model was instituted in an effort to be responsive to these trends. In addition to the layoff of support staff across multiple units that left managers struggling at times to keep libraries adequately staffed and open, remaining service desk staff were expected to refocus their priorities. Support staff, who for so long were evaluated on and lauded for their commitment to public service suddenly felt devalued and questioned their future in academic libraries. At the same time, librarians who were endeavouring to support new faculty services and library-based initiatives were left clamouring for support from library assistants who were now in short supply.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".