Highlighting the potential of peer-led workshops in training early-career researchers for conducting research with Indigenous communities
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.
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 | 0.043 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".