New opportunities for repositories in the age of altmetrics
Bibliographic record
Abstract
Abstract Editor's Summary For institutional repositories, alternative metrics reflecting online activity present valuable indicators of interest in their holdings that can supplement traditional usage statistics. A variable mix of built‐in metrics is available through popular repository platforms: Digital Commons, DSpace and EPrints. These may include download counts at the collection and/or item level, search terms, total and unique visitors, page views and social media and bookmarking metrics; additional data may be available with special plug‐ins. Data provide different types of information valuable for repository managers, university administrators and authors. They can reflect both scholarly and popular impact, show readership, reflect an institution's output, justify tenure and promotion and indicate direction for collection management. Practical considerations for implementing altmetrics include service costs, technical support, platform integration and user interest. Altmetrics should not be used for author ranking or comparison, and altmetrics sources should be regularly reevaluated for relevance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.129 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.029 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.054 | 0.112 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.029 | 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".