Using collaborative critical autoethnography to decolonise through “seeing” and doing: Social work, community engagement, and ethical practice
Bibliographic record
Abstract
INTRODUCTION: This article reports on a collaborative critical autoethnographic study that we, two white settler social workers, conducted about our engagement with Inuit youth in Nunavut. APPROACH: We facilitated three digital storytelling projects with youth living in three different Nunavut communities. By engaging in a collaborative critical autoethnography study, we were able to attend to the ways in which we were entering into communities, paying particular attention to the ways in which white supremist colonial thought has impacted our training and our locations within larger structures—shaped by colonising histories with consequences that mould day-to-day life and opportunity for the Inuit youth engaged in the digital storytelling. FINDINGS: Through collaborative critical autoethnography, using individual research memos and guided dialogue, we considered the ways in which commodification was structured into our relationships, how these structures continue to be colonising, and consider the impact of the past and current colonisation. We also encountered the many strengths and resistances of the Inuit of Nunavut. IMPLICATIONS: By bringing these considerations to light, we hope to enter into relationships with Inuit communities with fewer of the biases and assumptions that underlay and rationalise the structures that we have critically examined.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.026 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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".