Coverage and adoption of altmetrics sources in the bibliometric\n community
Bibliographic record
Abstract
Altmetrics, indices based on social media platforms and tools, have recently\nemerged as alternative means of measuring scholarly impact. Such indices assume\nthat scholars in fact populate online social environments, and interact with\nscholarly products there. We tested this assumption by examining the use and\ncoverage of social media environments amongst a sample of bibliometricians. As\nexpected, coverage varied: 82% of articles published by sampled\nbibliometricians were included in Mendeley libraries, while only 28% were\nincluded in CiteULike. Mendeley bookmarking was moderately correlated (.45)\nwith Scopus citation. Over half of respondents asserted that social media tools\nwere affecting their professional lives, although uptake of online tools varied\nwidely. 68% of those surveyed had LinkedIn accounts, while Academia.edu,\nMendeley, and ResearchGate each claimed a fifth of respondents. Nearly half of\nthose responding had Twitter accounts, which they used both personally and\nprofessionally. Surveyed bibliometricians had mixed opinions on altmetrics'\npotential; 72% valued download counts, while a third saw potential in tracking\narticles' influence in blogs, Wikipedia, reference managers, and social media.\nAltogether, these findings suggest that some online tools are seeing\nsubstantial use by bibliometricians, and that they present a potentially\nvaluable source of impact data.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.047 | 0.050 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".