Guidance List for Reporting Bibliometric Analyses (GLOBAL): A Two-Round Modified Delphi Study
Bibliographic record
Abstract
Background: Despite the growth of bibliometric analyses in the scholarly literature, few studies offer guidance on how to report them, resulting in a lack of transparency and completeness in research. To address this gap in thorough reporting practices, in accordance with existing best practice for establishing reporting guidelines, we developed the Guidance List for the repOrting of Bibliometric AnaLyses (GLOBAL), a reporting guideline aimed at promoting high-quality reporting of bibliometric analyses. Methods: An initial list of items for the GLOBAL was generated through a scoping review and further refined through a two-round Delphi, as outlined by the EQUATOR Network’s methodological framework on creating reporting guidelines. Participants, including international bibliometric experts, were recruited for the Delphi via personalized emails and open invitations. Consensus was achieved when at least 80% of participants agreed on the inclusion or exclusion of items in the GLOBAL checklist. Items that did not reach consensus were excluded. Round 1, conducted through an international online survey, used a 9-point Likert scale to assess how essential an item was for reporting bibliometric analyses. A content analysis was performed on participant feedback from Round 1, including comments on each item and responses to the openended questions. Round 2 consisted of an in-person meeting to discuss and vote on items that were new or did not reach consensus in Round 1. Results: In Round 1, 24 of 32 items reached consensus and content analysis resulted in one new item. This item and the eight items that did not reach consensus were discussed in Round 2. During the meeting, one item was split into two, totalling ten items. Nine out of ten items reached consensus, five for inclusion and four for exclusion, while 1 item was also excluded because it did not reach consensus. Conclusions: The finalized 29-item GLOBAL checklist provides users with guidance to report bibliometric analyses. Its international adoption is aimed at improving the reporting practices of bibliometric analyses for research purposes.
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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 | 0.389 | 0.454 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".