Early career researchers and their authorship and peer review beliefs and practices: An international study
Bibliographic record
Abstract
This article reports on the findings of an international online survey of early career researchers (ECRs) with regard to their authorship and peer review, attitudes, and practices, which sought to discover how the new wave of researchers were utilizing these key aspects of the scholarly communications system. A questionnaire was developed on the back of a 3‐year longitudinal, qualitative study and was distributed through publisher lists, social media networks, university networks, and specialist ECR membership organizations. Identical English, Polish, Russian, Chinese, Spanish, and French versions of the questionnaire were used. Results from 1,600 respondents demonstrated that 82.7% had co‐authored a paper, and most had performed a variety of authorship tasks. Almost half the respondents reported being subject to various authorship policies, although a quarter said they were not aware of any such policies. Almost all Chinese ECRs reported being subject to authorship policies, but only a third of UK ECRs reported the same. Three‐quarters of ECRs had experience in responding to peer review, and half had been peer reviewers. Half the respondents had a good experience of review and viewed it as a valuable way to improve their authorship skills. However, there was some criticism of some shortcoming such as lengthy peer review and superficial or uninformed comments by reviewers. Double‐blind review was the preferred methodology, and there were few suggestions for how to improve the review process.
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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 | 0.031 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".