Multi-dimensional research impact assessment through bibliometrics, altmetrics, semantometrics, and webometrics
Bibliographic record
Abstract
Research impact assessment (RIA) has emerged as a critical approach for evaluating the societal, academic, and policy-related influence of scholarly work, particularly within the evolving landscape of Open Science. This paper provides a synthesis of quantitative RIA metrics, which offer standardised, data-driven insights into the reach and significance of research outputs. It outlines four principal methodologies: (i) bibliometrics (analyse citation patterns through indicators such as citation counts, co-citation, and bibliographic coupling); (ii) altmetrics (track online engagement and dissemination); (iii) semantometrics (assess textual contributions using semantic similarity measures); and (iv) webometrics (evaluate digital presence through web interactions and backlink analysis). While these quantitative approaches are valuable for benchmarking and strategic decision-making, they often fail to capture the nuanced societal and intellectual impacts of research. To address this limitation, the paper advocates for a hybrid assessment model that integrates quantitative metrics with qualitative methods, such as case studies and narrative analyses, to provide both scalability and contextual depth. Ultimately, the work underscores the importance of critically and judiciously interpreting RIA metrics to fully reflect the multifaceted nature of research impact across disciplines and stakeholder domains.
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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 | 0.067 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.121 | 0.134 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".