Guidelines for ensuring meaningful engagement of early career researchers in scientific collaborations: recommendations from and for marine and polar scientists
Bibliographic record
Abstract
Abstract There is an increasing recognition of the importance of involving early career researchers (ECRs) in scientific positions of trust within national and international organizations, collaborative research projects, networks, and working groups. While the inclusion of ECRs in positions traditionally dominated by more established scientists is a welcome development to increase diversity, equity and inclusion in science, ECRs are often brought into different processes without consideration of the differences in career stages and unfamiliarity of newcomers to projects and processes. These challenges are particularly felt by ECRs with multiple disadvantaged statuses or identities (e.g. ECRs from ethnic minorities, the Global South, and those with caring responsibilities). This paper presents ten guidelines prepared as a participatory process of 12 marine and polar science early career networks, aiming to provide a comprehensive framework for various stakeholders involved in the academic and research ecosystem to improve ECR engagement in collaborations and institutional processes. These guidelines are intended to be adaptable to various contexts, ensuring that all those engaging with ECRs can effectively support their development and well-being. By following these guidelines, members of the scientific community and associated organizations can contribute to a nurturing and productive working environment that benefits the entire research community. This, in turn, will contribute to the long-term success of individual researchers, their institutions, and ultimately science itself.
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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 | 0.369 | 0.462 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.017 | 0.026 |
| Research integrity | 0.053 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".