The author who wasn’t there? Fairness and attribution in publications following access to population biobanks
Bibliographic record
Abstract
We conducted a document analysis that explored publication ethics and authorship in the context of population biobanks from both a theoretical (e.g. normative documents) and practical (e.g. biobank-specific documentation) perspective. The aim was to provide an overview of the state of authorship attribution in population biobanks and attempt to fill the gap in discussions around the issue. Our findings demonstrate that the most common approach adopted in both the normative and biobank-specific documentation is acknowledgment. A co-authorship approach was second and highlighted concerns surrounding the fairness of imposing authorship of the scientific leadership as a condition to access data and biosamples, as well as the alignment with the International Committee of Medical Journal Editors' criteria such as what is deemed a significant contribution and how to ensure accountability. Based on these findings, we propose a three-prong approach, that may be cumulative, to address the issue of authorship attribution in the context of population biobanks, namely 1) the biobank should be appropriately acknowledged; 2) an invitation for co-authorship should be made based on the spirit of collaboration and provided a substantial contribution has been made; and 3) a citation/referencing option should be available.
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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 | 0.418 | 0.680 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".