What to do when everyone wants to be your partner: transforming the faculty/librarian relationship
Bibliographic record
Abstract
Historically, academic librarians have worked very hard at being involved in the day today work of the faculty and have sometimes considered themselves lucky to be invited to teach ina class or to sit on a faculty council. However with the advance of evidence based practice andgrowth of systematic review searching as a form of research, and the requirement by somefunding agencies that librarians co-author on systematic reviews, academic health scienceslibrarians are facing exponential increases in the demand for their time. At the University ofAlberta's John W. Scott Health Sciences Library, a plan has been developed to manage thissignificant increase in the demand for librarians' time. The plan includes: ensuring that tasksare assigned at the correct level, building searcher capacity in the community, lobbying Facultyand Library Administrations to increase the number of librarian/expert searcher positions,defining policies on the extent and nature of services provided to specific user groups, betterorganizing search support resources and educating users. Early observational results showmoderate success in community capacity building and very high interest in instructionalprograms for client groups.
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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 | 0.044 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.032 | 0.015 |
| Scholarly communication | 0.034 | 0.033 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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".