Into the gray: a modified approach to citation analysis to better understand research impact
Bibliographic record
Abstract
Academic authors and funders often want to know the “impact” of their publications, and this impact is generally judged by how and where the paper is cited in other academic works. This limited appraisal has been expanded in recent years as many are beginning to argue that nonacademic publishing venues should be included in assessing the impact of academic publications. This is an issue of particular concern with the growing emphasis on “knowledge translation” from the scientific literature to policy and practice applications 1–3 and to sources other than the traditional peer-reviewed and indexed venues, in other words, translation into the “gray literature” 4. In this comment and opinion piece, the authors describe the process of developing and applying a “modified citation analysis” that builds on existing methods of examining a research paper's impact in two key ways: (1) by deliberately including gray literature in the citation analysis search process, and (2) by including quantitative and qualitative methods of analysis to gain a better understanding of how a research paper was used. By broadening the search and deepening the level of analysis, we suggest this new approach can better assess the impact of a given research paper—both within and outside of traditional peer-reviewed venues. We begin with a review of gray literature and then describe current methods for analyzing the impact of a research paper. Finally, we use a specific example to describe our new approach, highlight its potential for evolving the field of citation and impact analysis, and discuss future refinements and evaluation.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | BibliometricsMetaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.160 | 0.406 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.061 | 0.057 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.026 | 0.036 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".