International benchmarks for academic library use of bibliometrics & altmetrics: 2016-17
Bibliographic record
Abstract
This study presents data from 20 predominantly research universities in the USA, continental Europe, the UK, Canada and Australia/New Zealand. Among the survey participants are: Carnegie Mellon, Cambridge University, Universitat Politècnica de Catalunya the University at Albany, the University of Melbourne, Florida State University, the University of Alberta and Victoria University of Wellington. The report gives detailed data on the use of various bibliometric and altmetric tools such as Google Scholar, Web of Science, Scimago, Plum Analytics, and many, many others. The 114-page report presents detailed information on staffing, budgets, marketing, and sources of demand, technology and other factors in bibliometric and altmetric service development. Just a few of the report’s many findings are that: • Institutions cited by survey participants for excellence in bibliometrics or altmetrics were: Georgia State University, Yale University, the University of New South Wales, the National Library of Medicine and the University of Pittsburgh, among others. • 50% of the institutions sampled help their researchers to obtain a Thomsen/Reuters Researcher ID. • A 60 percent majority said demand for bibliometric services increased slightly, 10 percent said it increased considerably, and 5 percent said demand fell somewhat. A quarter of the participants said demand for bibliometric services at their institution remained about the same over the past two years. • Academic department heads accounted for a mean of 24.38% of the demand for bibliometric services from the libraries sampled. • Just 5% of those surveyed use Facebook Insights in their altmetrics efforts.
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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 | 0.038 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.048 | 0.119 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".