Renewing relationships with glocal lands: Exploration of Indigenous - Refugee Relations with the Canadian Centre for Victims of Torture
Bibliographic record
Abstract
This project, entitled Renewing relationships with glocal lands: Exploration of Indigenous - Refugee Relations with the Canadian Centre for Victims of Torture, explored Indigenous-refugee relations in Canada, particularly, the role of refugee communities in truth and reconciliation with Indigenous peoples. In 2015, Canada’s Truth and Reconciliation Commission (TRC) proposed 94 Calls to Action including the final two, #93 and #94, which address concerns related to newcomers to Canada. Item #93 calls for a more inclusive history of diverse Indigenous Peoples of Canada in settlement education and the Canadian citizenship test, including information about treaties and the history of residential schools. Meanwhile, item #94 calls for an update to the citizenship oath to include the acknowledgement of Indigenous presence regarding the existence of treaties. These calls to action are put forward with the key message of TRC that “[w]e are all Treaty people who share responsibility for taking action on reconciliation” (2015, p. 11). This was a unique community-engaged research project with a refugee serving organization, the Canadian Centre for Victims of Torture (CCVT), to explore what it means for refugee communities to uphold the responsibility of reconciliation and to think about decolonizing praxis in the everyday practice of refugee resettlement and integration. The project asked: What does refugee integration look like when local Indigenous history, presence, and knowledge are considered? We explored this question via a community action research methodology centered on land-based education from the Haudenosaunee perspective and focus groups with 8 participants from CCVT (a mix of clients, and staff members). The project was divided into three phases: the first phase was a pre-workshop focus group to examine the current understanding of refugee integration and Indigenous issues among the participants; the second phase of this research included six land-focused education workshops where the participants learned about local Indigenous histories, presence, and knowledge, and the final phase was a post-workshop focus group where participants discussed learnings from the workshops and their application to future service delivery of refugee integration including trauma healing and citizenship education.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.065 | 0.035 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".