Canadian Academic Librarians and the Need for a Systematic and Comprehensive Research-Support Model
Bibliographic record
Abstract
Expectations for Canadian academic librarians’ research have evolved, but not all librarians have access to the support systems needed that aid and enable them to conduct and publish research. A survey was sent to librarians asking about the research supports available and most useful to them. “Research” was not defined and was left to the interpretation of the participant. The survey found that supports are sporadic, possibly leading to a two-tiered research climate between “haves” and “have-nots.” It is essential for academic librarians to initiate and engage in conversation about what library research is and how librarians’ research competency may be improved. This should lead in turn to conversations about the support systems needed, which ones the universities and the libraries should provide, and how having a comprehensive research-support model would help librarians engage more with research, increase their research output, and improve the quality of their research.
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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 | 0.361 | 0.436 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.041 | 0.063 |
| Scholarly communication | 0.050 | 0.028 |
| Open science | 0.012 | 0.030 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier 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".