Indigenous and Deaf People and the Implications of Ongoing Practices of Colonization: A Comparison of Australia and Canada
Bibliographic record
Abstract
In the growing field of colonial and anti-colonial research, many parallels have been drawn between Westernized countries including Australia and Canada. In both of these countries, there is considerable academic, community and governmental recognition of historic, and continuing, colonizing of Indigenous peoples and the subsequent impacts on Indigenous cultures. Terms such as transgenerational trauma and intergenerational trauma give language to the ongoing impact of colonization on communities, which in turn serves to legitimize the need for mental wellbeing supports and associated funding. However, there are other minority communities that are similarly oppressed and colonized but do not experience the same legitimization. One such community is the Deaf community. Deaf people continue to experience systemic oppression and colonization within our hearing centric society. Building on the work of Batterbury, Ladd and Gulliver (2007), we extend discussions on the parallels between Indigenous and Deaf communities of Australia and Canada, drawing on the established and commonly discussed link between the impact of racism and colonization on (mental) health. We connect these discussions to modern instances of colonization including the aspect of deaf education to illustrate a “living” mechanism through which colonization continues to impact mental wellbeing in the broader Deaf community.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".