Kitchen Table Methodologies: How infrastructure deficits shape community-based research activities in the Canadian Arctic
Bibliographic record
Abstract
In this evolving era of community-engaged research, the knowledge, skills, and leadership of Arctic-based Peoples and organizations are extremely sought after by visiting researchers. Many conversations have established the ‘what’, ‘how’, and ‘why’ of community-engaged Arctic research, leaving the ‘where’ as a much less understood area. While some aspects of research take place in the ‘field’ (the ice, the water, the land), a great deal of research activities require functional indoor workspaces (the office, the community centre, the library). Despite their involvement being in high demand, Arctic-based researchers often do not operate with the same infrastructure offered in traditional research institutions such as universities. Though sophisticated research spaces such as research stations do exist in the Arctic, many are owned by southern-based institutions or governments. It is therefore important to understand where community-led research activities take place in the absence of access to formal research spaces. This case-study research sought to explore and document the spaces in which community-based research activities take place in Mittimatalik, Nunavut, and how these spaces impact the community’s ability to take part in, and lead research. Through interviews, workshops, and a photovoice, participants reported that a great deal of their research activities take place in the home. With Nunavut holding the highest rate of inadequate housing in Canada, this overlap implies a direct relationship between research activities, overcrowding, unaffordability, and insufficient infrastructure. This presentation will facilitate a much-needed understanding of the current state of, and relationship between, physical research spaces and community-based research activities in Arctic Canada.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.038 | 0.016 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".