Working and Thinking Across Difference: A White Social Worker and an Indigenous World
Bibliographic record
Abstract
Indigenous populations have experienced vast travesties due to the impacts of colonialism. Colonialism continues to be perpetuated through the services, programs and policies that Indigenous people encounter. This research thesis tackles the question of how non-Indigenous social workers, professionals and interested parties can work with Indigenous people in appropriate and respectful ways. It also reviews how non-Indigenous people can work and think across difference. This research represents my journey towards decolonizing myself to find new ways of being White that are compatible with Indigenous knowledge systems and ways of knowing. Autoethnography, relevant literature and interviews were used to explore ways of working with Indigenous populations. Three participants who had been identified by an Indigenous academic as people who had worked with Indigenous populations in appropriate and respectful ways were interviewed in Canada. An analysis of the three semi in-depth interviews produced several recommendations for non-Indigenous people in working with Indigenous populations. Results acknowledge the complexity of working and thinking across difference. Suggestions for working with Indigenous populations are highlighted and include such themes as acknowledging tensions and privilege, understanding that there is a large diversity within Indigenous populations, recognizing that there are aspects of dominant ways of knowing that are compatible with Indigenous ways of knowing, the importance of not being afraid to take risks and of trying not to make assumptions. Decolonization is an uneasy pursuit that is fraught with tension and this research hopes to encourage other social workers, professionals and interested parties to engage in similar processes.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.075 | 0.040 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".