Contributorship and division of labor in knowledge production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Scientific authorship has been increasingly complemented with contributorship statements. While such statements are said to ensure more equitable credit and responsibility attribution, they also provide an opportunity to examine the roles and functions that authors play in the construction of knowledge and the relationship between these roles and authorship order. Drawing on a comprehensive and multidisciplinary dataset of 87,002 documents in which contributorship statements are found, this article examines the forms that division of labor takes across disciplines, the relationships between various types of contributions, as well as the relationships between the contribution types and various indicators of authors' seniority. It shows that scientific work is more highly divided in medical disciplines than in mathematics, physics, and disciplines of the social sciences, and that, with the exception of medicine, the writing of the paper is the task most often associated with authorship. The results suggest a clear distinction between contributions that could be labeled as 'technical' and those that could be considered 'conceptual': While conceptual tasks are typically associated with authors with higher seniority, technical tasks are more often performed by younger scholars. Finally, results provide evidence of a U-shaped relationship between extent of contribution and author order: In all disciplines, first and last authors typically contribute to more tasks than middle authors. The paper concludes with a discussion of the implications of the results for the reward system of science.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.097 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it