ASIS&T annual meeting pre‐conference activities: Full room for the third SIG/MET workshop
Bibliographic record
Abstract
Abstract EDITOR'S SUMMARY SIG/MET presented its third Workshop on Informetric and Scientometric Research at the ASIS&T November 2013 Annual Meeting. Established in 2010, the group brings together those interested in all aspects of informetrics, including bibliometrics, scientometrics and webometrics, as well as metrics related to citation network analysis, visualization and scholarly communication. The meeting featured posters on measuring research in the context of academic monitoring and on the transition of meeting abstracts to peer‐reviewed journal articles. Thirteen papers were presented in sessions addressing the application of metrics and new indicators. A session on topics beyond the journal article included discussions on Twitter hashtag use, motivations for blog posts and advisees' career success relative to advisers' scholarly activity. SIG/MET recognized students for outstanding contributions on statistical analysis of citation rates, cognitive aspects of peer review and indicators for research evaluation. The symposium concluded with discussion of the availability of a Scopus dataset for arts and humanities journals for research use.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.155 | 0.114 |
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".