Involvement of local Indigenous peoples in Arctic research — expectations, needs and challenges perceived by early career researchers
Bibliographic record
Abstract
Rapid changes in the natural and social environments of the Arctic region have led to increased scientific presence across the Arctic. Simultaneously, the importance of involving local Indigenous peoples in research activities is increasingly recognized for several reasons, including knowledge sharing and sustainable development. This study explores Arctic early career researchers’ (ECRs) perceptions on involving local Indigenous peoples in their research. The results, based on 108 online survey respondents from 22 countries, show that ECRs value the knowledge of local Indigenous peoples and generally wish to extend the involvement of this group in their research. ECRs in North America and in the social sciences have more experience working with Indigenous communities and value it more than researchers in the Nordic area and in the natural sciences. Respondents cited more funding, networking opportunities, and time as the main needs for increasing collaborations. The results of this study are helpful for developing strategies to build good relationships between scientists and Indigenous peoples and for increasing the involvement of Arctic Indigenous peoples in science and engagement of their knowledge systems. The complementary views from Arctic Indigenous peoples are, however, needed for a full understanding of how to effectively achieve this.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".