The norms of authorship credit: Challenging the definition of authorship in The European Code of Conduct for Research Integrity
Bibliographic record
Abstract
The practice of assigning authorship for a scientific publication tends to raise two normative questions: 1) "who should be credited as an author?"; 2) "who should not be credited as an author but should still be acknowledged?". With the publication of the revised version of The European Code of Conduct for Research Integrity (ECCRI), standard answers to these questions have been called into question. This article examines the ways in which the ECCRI approaches these two questions and compares these approaches to standard definitions of "authorship" and "acknowledgment" in guidelines issued by the International Committee of Medical Journal Editors (ICMJE) and the World Association of Medical Editors (WAME). In light of two scenarios and the problems posed by these kinds of "real-world" examples, we recommend specific revisions to the content of the ECCRI in order not only to provide a more detailed account of the tasks deserving of acknowledgment, but to improve the Code's current definition of authorship.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.325 | 0.527 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.014 | 0.112 |
| Scholarly communication | 0.031 | 0.031 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.025 | 0.027 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".