What does it mean to be an author? The intersection of credit, contribution, and collaboration in science
Bibliographic record
Abstract
Abstract In this article, I draw on interview data gathered in the High Energy Physics (HEP) community to address recent problems stemming from collaborative research activity that stretches the boundaries of the traditional scientific authorship model. While authorship historically has been attributed to individuals and small groups, thereby making it relatively easy to tell who made major contributions to the work, recent collaborations have involved hundreds or thousands of individuals. Printing all of these names in the author list on articles can mean difficulties in discerning the nature or extent of individual contributions, which has significant implications for hiring and promotion procedures. This also can make collaborative research less attractive to scientists at the outset of a project. I discuss the issues that physicists are considering as they grapple with what it means to be “an author,” in addition to suggesting that future work in this area draw on the emerging economics literature on “mechanism design” in considering how credit can be attributed in ways that both ensure proper attribution and induce scientists to put forth their best effort.
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 | 0.038 | 0.228 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".