Capacity, scale and place: pragmatic lessons for doing community‐based research in the rural setting
Bibliographic record
Abstract
Community‐based research (CBR) represents a particularly timely approach to rural research. Rural areas in industrialized nations are undergoing dramatic and rapid processes of economic, social and political restructuring. These forces, combined with a trend towards place‐based development and territorial policy make CBR an appropriate rural method given its flexibility and sensitivity to local context. The purpose of this paper is to reflect on the use and methods of CBR in the rural setting, drawn from our collective research experience in northern British Columbia. There has been increased attention paid to CBR, signalling a form of acceptance within the academy towards community‐based and participatory methods. However, gaps exist in addressing the various approaches to conducting CBR and in considering the relevance of CBR in different contexts. Researchers also note the need for better training in the use of community‐based methods. We reflect upon our rural CBR experience to offer insights and pragmatic lessons on effective methodological practices using a simplified framework of the key research process stages: preparing for community engagement, doing community‐based research and after the fieldwork.
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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 | 0.161 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.030 | 0.153 |
| Scholarly communication | 0.030 | 0.042 |
| Open science | 0.008 | 0.036 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".