Altmetric Top 10: A Novel Approach to Marketing Research Impact Services
Bibliographic record
Abstract
Research impact services are becoming increasingly popular in academic libraries. However, there exist a number of barriers to the successful launch of such services. Misconceptions about what various indicators measure and how data will be used may make researchers wary of research impact services. Additionally, libraries may be hesitant to invest in expensive bibliometric/altmetrics databases without a known level of interest in such services. This intervention addressed these barriers by using the free version of Altmetric Explorer to launch a social media campaign highlighting the Faculty of Health Sciences’ Top 10 most attention-grabbing articles from the past year. The campaign served to simultaneously educate users about the calculation of Altmetric Attention Scores, celebrate the research efforts of the Faculty, and inform researchers of the existence of research impact services at the library. The two campaigns (run in January 2019 and 2020) generated considerable interest on Twitter and the library gained a number of influential followers as a result. In addition, the library saw a 39% increase in visits to its Research Impact subject guide, with spikes during the days the campaign was running. Such marketing campaigns represent a simple first step towards more fully-formed research impact services. The campaign served as a conversation starter and helped identify the library as the first stop for research impact support. What’s more, this research impact project required no additional funding and could easily be replicated by other libraries using their existing database subscriptions.
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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.127 | 0.039 |
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".