Day 1: Practical Applications of Altmetrics and Novel and Experimental Uses of Altmetrics
Bibliographic record
Abstract
Practical Applications of Altmetrics session: Chair: Hans Zijlstra, Elsevier - Leveraging altmetrics as opportunity indicators: David Sommer, Kudos - Using examples of high Altmetric scores as a roadmap for pre- and post-publication editorial support: Sacha Noukhovitch, STEM Fellowship - Can altmetrics data help researchers fine-tune their publication strategies? : Camilla Lindelöw, Södertörn University - Who’s talking about you? Using altmetrics in library assessment: Melanie Cassidy and Ali Versluis, University of Guelph Novel and Experimental uses of Altmetrics session: Chair: Andrea Michalek, PLUM/Elsevier - Temporal visualisation of altmetrics data across heterogenous sources: Waqas Khawaja, National University of Ireland Galway - ARIA: Aravind Raamkumar Sesagiri, Nanyang Technological University - Using altmetrics to highlight academic research: innovative possibilities: Rajiv Nariani, York University
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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 | 0.062 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.013 |
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".