Empowering Early Career Polar Researchers in a changing climate: Challenges and solutions
Bibliographic record
Abstract
Climate change is rapidly reshaping the research landscape in the polar regions.As such, Early Career Researchers (ECRs) face increasingly daunting challenges.These challenges include international and institutional competition for funding, shifting research demands and priorities, limited data sharing, and the need for strong mentorship.ECRs in polar research face various challenges that are common to other research fields; e.g., they can encounter difficulties in securing funding and publishing [1,2], which can lead to elevated stress and even job loss [3,4].Institutional barriers include pressure on senior scientists to retire prematurely and pervasive gender and social inequalities [5].Of the approximately 5,000 members of the Association of Polar Early Career Scientists (APECS), 57% identify as female [6].While specific data on gender distribution among APECS ECRs is unavailable, insights from the International Association for Hydro-Environment Engineering and Research (IAHR) reveal that women constitute 25% at all career stages, with ECRs representing 33%; this proportion exhibited minimal growth in recent years [7].Anticipated trends suggest a more balanced gender distribution among APECS ECRs relative to their counterparts at advanced career stages, with potentially greater gender imbalances in developing countries and certain research fields [7].Barriers to interdisciplinary polar research include demanding workloads, uncertain funding and employment prospects, and limited support for work-life balance [8].The benefits of open science practices for ECRs, including reputational gains and increased chances of publication, as well as the associated challenges have also been noted [9].In light of these challenges, addressing the climate and biodiversity crisis necessitates a reevaluation of academia's activities to ensure a safe and just space.This reevaluation calls for a balance that respects the environmental boundaries of the planet while ensuring social equity and justice [10].In response, we propose the following solutions: (i) Creating more funding opportunities directed toward ECR-led projects, such as the Scientific Committee on Antarctic Research (SCAR) fellowships or the research grants offered by the European Consortium for Ocean Research Drilling (ECORD), an integrated member of the International Ocean Drilling Programme (IODP); (ii) Increasing cooperation among research groups and local communities, with emphasis on decreasing the ecological footprint of fieldwork and increasing stakeholder
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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 | 0.051 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.019 | 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; 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".