Sometimes Two Hats Are Better Than One : Finding Synergy Between Collections and Liaison Work
Bibliographic record
Abstract
At the Gerstein Science Information Centre at the University of Toronto, collections work for all health science subjects has historically been the responsibility of a single liaison librarian. In December 2018, a new approach was implemented where two librarians share collections work for all health science subjects, while also supporting their own individual liaison portfolios. As the two new librarians hired into these positions, we initially had difficulty seeing the connection between what we felt were two distinct, unrelated roles. This poster will discuss how a project to investigate a new systematic review screening software challenged that perspective. Through the project, we were able to leverage the unique strengths of each role while learning to see and appreciate the intersection between them. In sharing our experiences, we hope to encourage continued conversation about the challenges and opportunities of split roles, and open the discussion with others who are working to balance collections work with additional responsibilities.
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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 | 0.213 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.030 | 0.038 |
| Open science | 0.003 | 0.043 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".