Bibliographic record
Abstract
Abstract Editor's Summary The ASIS&T Bulletin special section on altmetrics presents alternative metrics as a new and critically needed approach to measuring the impact of scholarly research. With long‐established citation‐based metrics unable to capture the increasing variety of online references to a scholar's work, alternative indicators offer a different view of the influence of that work. Contributed papers demonstrate how altmetrics can work on a personal level to enhance a scholar's CV and on a broad, even global level, to transform scholarly communication through its interaction with open access, digital repositories and research in emerging countries. One article suggests altmetrics should soon be included among mainstream metrics, and other contributions describe specific indicators and altmetric software considerations. The need for innovative measurement and the advantages of altmetrics in particular bode well for their wide acceptance and continuing development.
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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 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.020 |
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".