Guidance for the reporting of bibliometric analyses: A scoping review
Bibliographic record
Abstract
Abstract Despite the growth in the number of bibliometric analyses published in the peer-reviewed literature, few articles provide guidance on methods and reporting to ensure reliability, robustness, and reproducibility. Consequently, the quality of reporting in existing bibliometric studies varies greatly. In response, we are developing a preliminary Guidance List for the repOrting of Bibliometric AnaLyses (GLOBAL), a reporting guideline for bibliometric analyses. This paper outlines a scoping review that aims to identify and categorize bibliometric recommendations from the literature to develop an initial list of candidate items for GLOBAL. Five bibliographic databases, three preprint servers, and gray literature were systematically searched. Twenty-three out of 48,750 records fulfilled the inclusion criteria. Six documents contained bibliometric reporting recommendations based on a complete or partial literature review; all other sources (n = 17) contained opinion-based recommendations. A 32-item recommendation list that will inform the development of GLOBAL was created. A paucity of evidence-based studies on bibliometric reporting exists in the literature, supporting the need to create a reporting guideline for bibliometric analyses. The next step in GLOBAL project will focus on conducting a two-round Delphi study to achieve consensus on which of the 32 items should be included in GLOBAL.
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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 | 0.695 | 0.825 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.089 | 0.073 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.012 | 0.014 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".