Arctic Peoples and Beyond: research opportunities in neuroscience and behaviour
Bibliographic record
Abstract
OBJECTIVES: Arctic and northern peoples are spread across Alaska, Canada, Russia and the Scandinavian countries. Inhabiting a variety of ecosystems, these 4 million residents include Indigenous populations who total about 10% of the population. Although Arctic peoples have very diverse cultural and social systems, they have health issues related to environmental impacts and knowledge/treatment disparities that are common to other minority and Indigenous peoples around the world. Research that explores the neuroscience and behavioural aspects of these health disparities offers challenges and significant opportunities. As the next generation of neuroscientists enter the field, it is imperative that they view their contributions in terms of translational medicine to address health disparities. STUDY DESIGN: A workshop was designed to bring neuroscientists together to report on the current directions of neuroscience research and how it could impact health disparities in the North. This workshop produced research recommendations for the growth of neuroscience in the North. METHODS: On May 31, 2006 the National Institute of Neurological Disorders and Stroke, the Burroughs Wellcome Foundation, the Arctic Division of AAAS and the University of Alaska co-sponsored a workshop entitled "Arctic Peoples and Beyond: Decreasing Health Disparities through Basic and Clinical Research." Also, the role and goals of the International Union for Circumpolar Health (IUCH) were presented at the meeting. RESULTS: A set of recommendations related to research opportunities in neuroscience and behaviour research and ways to facilitate national and international partnerships were developed. CONCLUSIONS: These recommendations should help guide the development of future health research in circumpolar neuroscience and behaviour. They provide ideas about research support and informational exchange that will address health challenges.
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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 | 0.026 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".