A “Partnership” for the Professional Development of Librarian Researchers
Bibliographic record
Abstract
In this article the authors introduce the Librarians’ Research Partnership (LRP), founded in 2013, at McGill and Concordia University Libraries. The Partnership was inspired by the Canadian Association of Research Libraries’ Librarians’ Research Institute (CARL LRI) which was attended by three of the authors in 2012 and is described here from the point of view of the participants. The authors touch upon the research culture at McGill and Concordia Libraries and discuss barriers and supports for research as prominent themes in the literature on the research role of Canadian academic librarians. The formation of the LRP and the eight subsequent meetings are explained in detail, as well as the factors that made the LRP a successful initiative between the two universities: physical proximity, similarity of working environments, and common organizational culture. The article also includes a discussion of how the LRP’s philosophy might diverge from that of the LRI.
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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 | 0.069 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".