An Environmental Scan of Bibliometrics and Research Impact Open Instructional Trends at Canadian Academic Research Libraries
Bibliographic record
Abstract
While bibliometrics have been used for years in academia, recent conversations into their responsible use have driven a need for greater understanding of bibliometrics and research impact within the academic community. Academic librarians are ideal individuals to contribute to instruction on bibliometrics, as they are already embedded within their academic community’s scholarly processes and are often familiar with relevant tools and their functions. The purpose of this environmental scan was to evaluate the current state of open instructional materials for bibliometrics and research impact at the Canadian Association of Research Libraries (CARL) academic member institutions. An environmental scan of research guides was chosen as a methodology for this study. Results of this scan identify that 97% (28/29) of CARL academic member institutions held at least one research guide related to bibliometrics and research impact, in a total of 56 guides reviewed. A keyword analysis revealed that of the guides reviewed, keywords related to tools and methodologies of bibliometrics and research impact were discussed at the highest frequency (present within 96% of guides), while keywords related to responsible and alternative metrics were discussed at lowest frequency (present within 38% of guides). Results of this article will benefit 1) practicing librarians who are creating or updating their own bibliometrics and research impact guides or developing library instruction on related topics and 2) strategic planning and governance within academic institutions and more broadly at the national level by revealing trends in bibliometrics and research impact services and resources in the Canadian context.
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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 | 0.018 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.053 | 0.091 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".