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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".