NWB2024_Relative contributions of co-authors to scientific research papers
Bibliographic record
Abstract
Scientific research is nowadays overwhelmingly carried out as teamwork. Most research papers in the sciences are co-authored. But little is known about how much co-authors contribute to joint papers. Is there a typical distribution or pattern of contributions? Can contribution percentages be inferred reasonably accurately from the number and order of authors? A good understanding of these issues is crucial for fair assessments, qualitative and quantitative, of researchers. But because author contributions naturally cascade up to higher level aggregates, the implications are also profound the evaluation of research groups, departments, and research organizations. In this talk we will present an analysis of a unique, large-scale dataset of authors’ disclosures of estimates of their own contribution to co-authored papers in quantitative terms. These come from applicants to PhD and post-doc fellowships at Canadian funding agencies (CIHR, NSERC, SSHRC) and comprise ca. 40,000 contribution statements by about 5000 applicants in all disciplines of science and scholarship. We statistically describe the empirical contribution data and furthermore use it as criterion data to test the validity of bibliometric counting methods as instruments for measuring co-author contributions. To this end we compare values of the ‘fractional counting’ method, somewhat of a default method in professional bibliometrics, and values of other rarely used alternatives with the empirical data in a correlational analysis. Science-political implications of the results will be discussed.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.142 | 0.132 |
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".