Author ordering and citation-based measures of scholarly impact
Bibliographic record
Abstract
Abstract Objective To assess the use of common author ordering conventions, their effects on measures of citation impact, and their implications for the assessment of individual researchers and researcher rankings. Design Analysis of associations between the use of author ordering conventions, measures of citation impact, and researcher rankings using publications by Canadian primary health care researchers included in the Scopus database. Setting Canada. Participants The 49 living Canadian primary health care researchers with the most first-author citations. Main outcome measures Spearman rank correlations were assessed between rankings based on number of first-author citations and alternative measures of number of citations. Changes in researcher rankings were assessed based on alternative citation metrics. Results Rank order correlations varied from 0.55 (first author vs h index) to 0.83 (first-author citations vs first- and second-author citations). The proportion of researchers whose rankings changed by 12 or more ranks (25% or greater absolute change) compared to rankings based on first-author citations varied from 14% for rankings based on first- and second-author citations to 51% for rankings based on h index. Conclusion The variability and inconsistency of author ordering thwart efforts to identify or create valid measures to rank citation impact. Adoption of author ordering based on contribution as a universal convention would enhance the reliability and validity of comparisons and rankings across disciplines and research fields and would facilitate comparisons among candidates for hiring, tenure, promotion, and awards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.082 | 0.372 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.039 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".