Altmetrics: present and future
Bibliographic record
Abstract
Scholars are increasingly incorporating social media tools like blogs, Twitter, and Mendeley into their professional communication. Altmetrics tracks usage of these and similar tools to measure scholarly influence on the social Web. Altmetrics researchers and practitioners have amassed a growing body of literature and working tools to gather and analyze altmetrics and there is growing interest in this emerging subfield of scientometrics. In this panel, sponsored by SIG/MET, we will present results demonstrating the utility of alternative metrics from a variety of stakeholders: researchers, librarians, publishers and those participating in academic social media sites. We will discuss and debate the value and validity of such metrics with strong degrees of participation from the audience encouraged. Metrics, for better or worse, have had a presence in the lives of scholars--we will discuss the challenges and opportunities of altmetrics for the future.
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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 | 0.143 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.025 | 0.049 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.021 | 0.051 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".