Riding the crest of the altmetrics wave: How librarians can help prepare faculty for the next generation of research impact metrics
Bibliographic record
Abstract
As scholars migrate into online spaces like Mendeley, blogs, Twitter, and more, they leave new traces of once-invisible interactions like reading, saving, discussing, and recommending. Observing these traces can inform new metrics of scholarly influence and impact -- so-called "altmetrics." Stakeholders in academia are beginning to discuss how and where altmetrics can be useful towards evaluating a researcher's academic contribution. As this interest grows, libraries are in a unique position to help support an informed dialog on campus. We suggest that librarians can provide this support in three main ways: informing emerging conversations with the latest research, supporting experimentation with emerging altmetrics tools, and engaging in early altmetrics education and outreach. We include examples and lists of resources to help librarians fill these roles.
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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 | 0.107 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.054 | 0.080 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 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".