Bibliographic record
Abstract
With an increasing focus on scholarly communications in academic libraries, librarians are struggling with how best to support faculty with the location, interpretation, and appropriate use of metrics. Very little has been written about the faculty researcher perspective on metrics and, as a result, librarians may have a deep knowledge of the tools but have a more limited understanding of the users’ viewpoint. Seventy-nine senior research faculty who were five or more years post-tenure were interviewed. Faculty from the Humanities, Social Sciences and Sciences were all invited to participate. Each interview consisted of nine questions relating to how the faculty understand and use impact metrics in their academic life. Responses were varied to all of the questions and were tied closely to the disciplinary fields of the research faculty interviewed. A large majority of the interviewed faculty viewed the library as a key resource for getting more information relating to metrics. This research reveals a need to fill a gap between librarians and faculty researchers with examples of disciplinary best practices of metrics use, as well as product information as pertains to impact metrics.
 Les bibliothèques universitaires mettent de plus en plus l’accent sur la communication savante. Ce faisant, les bibliothécaires se demandent comment mieux appuyer le corps professoral en ce qui a trait au repérage, à l’interprétation et à l’utilisation appropriée d’indicateurs bibliométriques. Très peu de recherche portant sur la perspective des chercheurs sur ces mesures existe. En conséquence, les bibliothécaires peuvent avoir une connaissance profonde des outils mais une compréhension plus limitée du point de vue des usagers. Soixante-dix-neuf professeurs ayant obtenu leur permanence depuis au moins cinq ans ont été interviewés. Des chercheurs provenant des facultés des sciences humaines, des sciences sociales et des sciences ont été invités à participer. Chaque entrevue comprenait neuf questions portant sur la compréhension et l’utilisation des mesures d’impact dans leur vie universitaire. Les réponses à toutes les questions étaient variées et en lien avec le champ disciplinaire des chercheurs interviewés. Une majorité importante des interviewés percevait la bibliothèque comme une ressource significative pour obtenir plus de renseignements au sujet des indicateurs. Cette étude révèle un besoin de combler un vide entre les bibliothécaires et les chercheurs avec des exemples des meilleures pratiques pour l’utilisation des indicateurs au sein des disciplines ainsi qu’avec des informations sur les produits utilisés pour obtenir des mesures d’impact.
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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: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.102 | 0.213 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.039 | 0.030 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.034 | 0.055 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".