Analyzing the relationship between Altmetric score and literature citations in the Implantology literature
Bibliographic record
Abstract
BACKGROUND: The influence of research has long been studied using citations and impact factors (IFs). Electronic media is changing how people interact with the scientific literature. There are few investigations into these trends. PURPOSE: To explore whether Altmetrics correlate with traditional bibliometrics in the Implantology literature. MATERIALS AND METHODS: Five Implantology journals with the highest IF and the 10 most highly-cited articles within those journals from 2013 to 2016 were reviewed. Altmetric score, citation count, and media "mentions" were recorded. Comparisons were conducted between Altmetric score, citations, and IF by performing Pearson correlation coefficients and descriptive statistics. Twitter accounts were studied and compared to other metrics. RESULTS: Analysis revealed no correlation between citations and Altmetrics (r = .096,P = .506) or IF and Altmetrics (r = .111,P = .443) in 2013. Altmetrics were also not significantly correlated with citations (r = 0.148,P = .305) or IF (r = .145,P = .315) in 2016. Total Altmetric scores were nine times higher in 2016 compared to 2013, with news outlets and Twitter seeing large increases in mentions. Twitter was the top medium receiving mentions across the two cohorts. CONCLUSIONS: Compared to other fields, Implantology articles received lower Altmetric scores, noting an area of improvement. Altmetrics at this time are insufficient to replace traditional bibliometrics, but may provide helpful real-time information concerning article dissemination.
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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 | 0.013 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.047 | 0.059 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".