Building strategic partnerships to support research impact & discovery in Canadian Research Libraries
Bibliographic record
Abstract
This paper will update the international community on how Canadian research libraries are moving from supporting to taking a strong leadership role in key components of the University’s digital research support infrastructure and, in doing so, expanding the campus’ understanding of what good research libraries can and should do. The research will include real-world examples of research libraries leading campus implementations of Research Information Management Systems (RIMs) or Campus Research Information Management Systems (CRISs) to help document the institution’s research output; to partnering or leading the introduction of Bibliometrics services as a strategy for quantifying research impact; and to overseeing campus planning for research data management. The paper will outline some of the technology solutions being used across the country, but will focus most of the emphasis on the strategic relationships, service philosophies and project management expertise libraries are bringing to the table. The paper will also seek to flag the new competencies required to be successful in this new and rapidly changing environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.063 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.037 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.029 | 0.077 |
| Open science | 0.015 | 0.012 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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; both teacher heads agree on what is shown here.
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".