Highlighting the potential of peer-led workshops in training early-career researchers for conducting research with Indigenous communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For decades, Indigenous voices have called for more collaborative and inclusive research practices. Interest in community-collaborative research is consequently growing among university-based researchers in Canada. However, many researchers receive little formal training on how to collaboratively conduct research with Indigenous communities. This is particularly problematic for early-career researchers (ECRs) whose fieldwork often involves interacting with communities. To address this lack of training, two peer-led workshops for Canadian ECRs were organized in 2016 and 2017 with the following objectives: ( i) to cultivate awareness about Indigenous cultures, histories, and languages; ( ii) to promote sharing of Indigenous and non-Indigenous ways of knowing; and ( iii) to foster approaches and explore tools for conducting community-collaborative research. Here we present these peer-led Intercultural Indigenous Workshops and discuss workshop outcomes according to five themes: scope and interdisciplinarity, Indigenous representation, workshop environment, skillful moderation, and workshop outcomes. Although workshops cannot replace the invaluable experience gained through working directly with Indigenous communities, we show that peer-led workshops can be an effective way for ECRs to develop key skills for conducting meaningful collaborative research. Peer-led workshops are therefore an important but insufficient step toward more inclusive research paradigms in Canada.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it