Ukrainian Language Education Network: A Case of Engaged Scholarship
Bibliographic record
Abstract
Academics widely understand participatory action research (PAR) to be relevant to communities, collaborative from project design to dissemination of results, equitable and participatory while also action-oriented in pursuit of social justice. In this article, we suggest that there is much need to address both the challenges and opportunities that researchers encounter when applying participatory tools within an Indigenous context. In September 2013, the University of Victoria research team began a transportation safety project in partnership with the University of Windsor and participating Indigenous communities across the country. This project entailed both quantitative and qualitative research methodologies, including a national survey in addition to community conversations, to promote community health and injury prevention. Responsible for outreach to coastal communities in British Columbia, the interdisciplinary research team employed PAR methodologies to address local and national transportation safety concerns ranging from booster seat use to pedestrian safety. In this paper, we ask: what can participatory approaches offer the study of community-engaged research (CER) with Indigenous communities? First, we assess the promises and perils of PAR for community-engaged research when working with Indigenous communities; second, we aim to demystify the process of PAR based on our experience working with the Tsawout First Nation to “Light up the Night” through participatory video with Indigenous youth; third, we reflect on what we learned in this process and discuss avenues for further research. Our submission entails a written article and accompanying videos that illuminate the creative approach to collaborative engagement with Indigenous communities.
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How this classification was reachedexpand
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 | 0.900 | 0.815 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.474 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.788 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".