Reframing indigeneity: community participation to inform the development of an indigenous identity measure
Bibliographic record
Abstract
Much of the research done on, rather than with, Indigenous peoples has led to the misinterpretation of Indigenous identity by mainstream society and academic researchers. Imagemakers of early Canadian history put forth misinformation about the “Imaginary Indian,” which set the basis for how many Canadians and those in academia still view Canada’s national history (Cronin, 2003; Francis, 1992). This research sought to understand how Indigenous peoples on the Laurentian University campus in Sudbury, Ontario defined their own Indigeneity. It is hoped that the results of this thesis will inform the development of an Indigenous Identity Measure (IIM) as well as reframe conceptions of Indigenous identities from the viewpoint of Indigenous participants. I employed Sharing Circles as a community participatory method, with an integrated approach to data analysis (Bradley, Curry & Devers, 2007; Nabigon, Hagey, Webster & MacKay, 1999). The purpose of choosing relevant Indigenous methods was to privilege the Indigenous perspective. The Sharing Circles effectively demonstrated the diversity and complexity of how the participants understood and expressed their Indigenous identity. Culture, Colonization and Selfdetermining identity, were vital themes to help understanding Indigeneity. Lastly, by asserting self-determined Indigenous identities and by supporting decolonizing methodologies, this research can serve as a template to reframe conceptions of Indigenous identity in the hope that this information might be useful for Indigenous persons, administrators, researchers and professors on a university campus.
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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 | 0.071 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".