Reaching the hinterlands? COVID-19’s unexpected challenges to conducting participatory research on Inuit Arctic politics
Bibliographic record
Abstract
The Arctic is a strange sort of hinterland. While geographically distant from most global decision-makers, its role in global economics—and therefore politics—are increasingly centered in international dialogue. Early 2020, 2 years into a 4-year project studying the political organization and strategies of Canadian and American Inuit, a policy project’s research design and methodology were thrown into upheaval due to the COVID-19 pandemic. Travel to the Far North became impossible, and already stressed participants became at risk of even greater burnout. These changes necessitated rapid pivoting and methodological flexibility to finish data collection in a rigorous way to allow for subsequent trustworthy thematic content analysis. Subsequent methodological choices and use of digital technologies demonstrated the importance of design flexibility and benefits of data stream merging: findings validated across multiple data sources were more trustworthy. The process also necessitated continuous researcher self-evaluation of whether data collection practices truly enhanced the project. These lessons learned may inform future projects involving Indigenous communities or other populations at risk of disproportionate participant burnout and support the accessibility of projects involving remote populations. Further, they encourage centering research design choices around respect for participants.
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 | 0.141 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.022 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".