Shifting Perspectives in Polar Research: Global Lessons on the Barriers and Drivers for Securing Academic Careers in Natural Sciences
Bibliographic record
Abstract
The polar regions provide valuable insights into the functioning of the Earth’s regulating systems. Conducting field research in such harsh and remote environments requires strong international cooperation, extended planning horizons, sizable budgets and long-term investment. Consequently, polar research is particularly vulnerable to societal and economic pressures during periods of austerity. The global financial crisis of 2008, and the ensuing decade of economic slowdown, have already adversely affected polar research, and the current COVID-19 pandemic has added further pressure. In this article we present the outcomes of a community survey that aimed to assess the main barriers and success factors identified by academic researchers at all career stages in response to these global crises. The survey results indicate that the primary barriers faced by polar early and mid-career researchers (EMCRs) act at institutional level, while mitigating factors are developed at individual and group levels. Later career scientists report pressure toward taking early retirement as a means of institutions saving money, reducing both academic leadership and the often unrecognized but vital mentor roles that many play. Gender and social inequalities are also perceived as important barriers. Reorganization of institutional operations and more effective strategies for long-term capacity building and retaining of talent, along with reduction in non-research duties shouldered by EMCRs, would make important contributions toward ensuring continued vitality and innovation in the polar research community.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".