An Examination of Bibliometrics in Calls for Major Canadian Research Awards
Bibliographic record
Abstract
This study aimed to determine if bibliometrics are being sought as part of research award competitions, through an examination of calls for fifteen major Canadian research awards. This study further aimed to determine if there were indications that including bibliometrics in the award application process could be helpful towards a nominee’s success. In so doing, this paper contributes a Canadian perspective to a growing body of related research which has sought to address the lack of clarity in funding application assessment criteria and the role bibliometrics can play in addressing this issue.The study revealed no explicit requests for bibliometric indicators in the calls for nominations for the major research awards examined. Nevertheless, requests for nominees’ CVs and/or publication histories, which can serve as one basis for the bibliometric evaluation of performance, were regularly observed, as were mentions of interest in internationality, which can in turn be illustrated with the use of bibliometrics. Additionally, a prevalence of multidisciplinary review panels was observed, pointing to potential utility of normalized bibliometric indicators in the award nomination process. These findings suggest that there are aspects of award calls that correspond to existing bibliometric indicators, and so their use may be warranted eventhough, so far, they have not been asked for by name.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometrics Domain: Incentives · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | low |
| gpt | BibliometricsMetaresearch Domain: Evaluation · Genre: Other About the Canadian research system: yes · About a Canadian topic: yes | Not applicable | high |
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 | 0.121 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.198 | 0.151 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.028 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".