A new generation of Early Career Researchers in atmospheric chemistry: Navigating a globalized scientific landscape
Bibliographic record
Abstract
As the International Global Atmospheric Chemistry (IGAC) project marks its 35th anniversary, this paper examines the multifaceted experiences of Early Career Researchers (ECRs) navigating the increasingly globalized landscape of atmospheric chemistry. Drawing upon collective insights from the ECR Scientific Steering Committee and quantitative data from a survey of 180 ECRs across 40 countries, we investigate their primary motivations, challenges, and opportunities. Key obstacles identified include systemic difficulties in securing funding and resources, achieving sustainable work–life balance, and uncertainty around long-term career prospects, often compounded by precarious employment conditions. While globalization offers significant avenues for international collaboration, data sharing, and knowledge exchange, it concurrently presents challenges such as heightened competition, visa restrictions, regional disparities, and the risk of inequitable research practices. Despite these hurdles, ECRs are driven by a strong interest in their field, a desire to make a tangible impact on societal concerns, and the appeal of a supportive community. This perspective paper offers actionable insights focused on 4 key pillars: (i) strengthening mentoring programs; (ii) reforming funding mechanisms for improved accessibility and equity; (iii) providing targeted skill development workshops; and (iv) promoting equitable collaborations and advancing the decolonization of research practices. To foster a supportive, inclusive, and sustainable environment for the next generation of atmospheric scientists, this work underscores the urgent need for systemic change and sustained collaborative action by networks such as IGAC, as well as by senior researchers, academic institutions, and funding agencies.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".