Multidisciplinary Workshops for Early Career Researchers in Arctic Social Sciences and Engineering
Bibliographic record
Abstract
In early 2023, a series of workshops, funded by UArctic and EU H2020 Nunataryuk, were held in several Arctic communities, including Ilulissat (Greenland), Inuvik and Aklavik (Canada), and Longyearbyen (Svalbard). These workshops enabled Early Career Researchers (ECRs) in Arctic social, health, and engineering sciences to disseminate their research results to the communities. Additionally, the workshops provided a platform for the ECRs, senior scientists, local and Indigenous rights- and stakeholders participating in the events to engage in multidirectional knowledge exchanges on permafrost thaw risks and adaptation. In particular, sessions were held to gather local perceptions of risks and associated impacts. This report provides detailed summaries of the workshops, discussed topics and involved participants.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 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".