Arctic Conferences:What is the economic impact of choosing locations outside the Arctic to discuss Arctic issues?
Bibliographic record
Abstract
Arctic conferences are a unique setting where representatives of institutions/Indigenous peoples, stakeholders, politicians, scientist/young researchers, activists, and Arctic enthusiasts can meet while still having something in common. While there are hundreds of varied sizes, themes, and formats of Arctic conferences, before the global pandemic the number and variety of Arctic conferences were steadily growing in the world. But what are the impacts of these experiences and what is the value of holding these conferences in the Arctic itself? This article examines and analyzes the correlation between a number of Arctic conferences that were held specifically in the Arctic and in central regions of Canada, Finland, Norway, and Russia between 2012 and 2021. The data collection results identify a difference in the number of participants, focuses, investments, and potential regional impacts between conferences in the Arctic-regions versus those in centers or major cities. This article seeks to answer the question does the economic impact of Arctic conferences contribute to Arctic regional development? Additionally, this article highlights potential economic losses of the Arctic regions due to the ongoing organization of international Arctic events outside of the Arctic region.
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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".