The Meaning of Author Order in Medical Research
Bibliographic record
Abstract
BACKGROUND: Manuscript authorship and author placement have important implications for accountability and allocation of credit. The objective of this study was to assess the relationship between an author's place in the author list and the type of contribution reported by that author. This pattern was then used to develop a method by which author responsibility and accountability can be clarified. METHODS: The published contributions of each author of original research articles with a minimum of four authors published in the Journal of the American Medical Association, the British Medical Journal, The Lancet, and the Canadian Medical Association Journal in a 3-year period after author contribution forms were required were coded into 1 of eleven contribution categories. The contributions were grouped according to first, second, middle, and last author and compared by position. RESULTS: For most categories of contribution, the levels of participation were highest for first authors, followed by last and then second authors. Middle authors had lower levels particularly in conception, drafts of the manuscript, supervision, and being a guarantor. CONCLUSIONS: Current patterns of author order and contribution suggest a consistent theme. Based on the results, a proposal is put forth by which author accountability is clarified. In this proposal, authors are classified as either "primary," "contributing," or "senior or supervisory," each with specified contributions. More than one author may be classified into each author category.
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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 | 0.220 | 0.511 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.010 | 0.064 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".