Putting the community in community engagement in an urban Indigenous context
Bibliographic record
Abstract
Overall, the research aimed to situate urban Indigenous perspectives and experiences within the field of community engagement and inform the practice of urban Indigenous community engagement. The research is focused on understanding the ways that urban Indigenous people in Edmonton are involved in efforts to address the challenges they face. As well as, whether a decolonizing lens informs engagement processes in an urban Indigenous context. For several decades, Indigenous organizations in urban centers have been working to address socio-economic inequality created by the impacts of colonization. The elimination of poverty and broader inequality is an important aspect of improving the overall wellbeing of Indigenous peoples. Public engagement is one of the tools used by government to identify issues, needs and priorities on various issues, however, this type of engagement has become a repetitive cycle that has not resulted in the kinds of changes desired by the urban Indigenous community. How can engagement processes better ensure the inclusion of the diversity of urban Indigenous peoples’ voices and perspectives and counter their marginalization? The research project used qualitative methods to capture Indigenous perspectives on their experience participating in engagement processes. The research project used Indigenous Research Methodologies (IRM) which has a decolonizing agenda that aims to further social justice. Further, IRM is informed by relational accountability, positionality and reflexivity as well as privileging the experience and knowledge of Indigenous peoples. The methods include the use of semi-structured interviews with urban Indigenous people who have experience with local engagement processes and thematic analysis was used in the analysis of the data. The research findings highlight Indigenous perspectives on identity and connection, experiences with engagement and examples of successes and challenges specific to EndPovertyEdmonton. The participants’ experiences with engagement are reflected within the five themes of representation, relational, meaningful involvement, action-orientated and self-determination. The implications of the findings point toward an emphasis on community-based approaches when working within an urban Indigenous context. That at a minimum is based on co-production but ideally supports self-determination. At the same time, the urban Indigenous population need the time and space to engage amongst ourselves. In the conclusion wise practices to working with urban Indigenous populations are put forward that align with the five themes from the research findings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".