Academic Librarians’ Knowledge of Bibliometrics and Altmetrics
Bibliographic record
Abstract
Objective – To measure the knowledge and opinions that academic librarians have of established and emerging research metrics. Methods – An online survey was distributed to all academic librarians in Oklahoma during Summer 2015. Results – Librarians were less familiar with altmetrics than with bibliometrics, but they viewed altmetrics as effective and were interested in receiving training to learn more about them. Librarians who had been in the profession for over five years knew more about both bibliometrics and altmetrics than newer librarians. Conclusions – Technological advances and changes in the ways that research products are shared have led to the possibility of and need for new ways of measuring research impact. Altmetrics have emerged to fill this need, but academic librarians need more familiarity and training to be able to fulfill a role as providers of these metrics.
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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 | 0.074 | 0.281 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".