Perspectives from early career researchers on the publication process in ecology - a response to Statzner & Resh (2010)
Bibliographic record
Abstract
1. Two senior ecologists summarised their experience of the scientific publication process (Statzner & Resh, Freshwater Biology, 2010; 55, 2639) to generate discussion, particularly among early career researchers (ECRs). As a group of eight ECRs, we comment on the six trends they described. 2. We generally agree with most of the trends identified by Statzner & Resh (2010), but also highlight a number of divergent perspectives and provide recommendations for change. Trends of particular concern are the use of inappropriate metrics to evaluate research quality (e.g. impact factor) and the salami slicing of papers to increase paper count. We advocate a transparent and comprehensive system for evaluating the research. 3. We stress the importance of impartiality and independence in the peer review process. We therefore suggest implementation of double-blind review and quality control measures for reviewers and possibly editors. Besides such structural changes, editors should be confident to overrule biased reviewer recommendations, while reviewers should provide helpful reviews but be explicit if a submission does not meet quality standards. Authors should always conduct a thorough literature search and acknowledge historical scientific ideas and methods. Additionally, authors should report low-quality copy editing and reviews to the editors. 4. Both early and late career researchers should jointly implement these recommendations to reverse the negative trends identified by Statzner & Resh (2010). However, more senior scientists will always have to take the lead with respect to structural changes in the publication system given that they occupy the majority of decision-making positions.
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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 | 0.240 | 0.394 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.035 | 0.047 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.052 | 0.066 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".