An Indigenous Self-Declaration Relational Policy Framework
Bibliographic record
Abstract
Canadian historical records demonstrate the role of schools in diminishing Indigenous identity, either intentionally or as a result of neglect, within dominant western systems (Battiste, 2013; Harper & Thompson, 2017; Henry et al., 2017; Marom, 2019; Pidgeon et al., 2013; St. Denis, 2011). Despite the oppressive effects of institutional racism (Gillies, 2021; Harper & Thompson, 2017; Henry et al., 2017; Marom, 2019; McLean, 2022) and policies that limited the participation and influence of Indigenous people in publicly funded Canadian schools, Indigenous educators have maintained a presence in schools, contributing positively to Indigenous students’ experiences (Battiste, 2013; Burgess & Cavanagh, 2015; Gillies, 2021; Keddie, 2013; Santoro, 2015; St. Denis, 2011). While the Saskatchewan socio-political environment is increasingly characterized by reconciliation and expectations of Indigenous participation (Ministry of Education, 2019), our study identified that the provincial school policy environment is largely silent on the role of Indigenous educators in meeting system goals and on indications of how school divisions navigate issues of Indigenous identity and authenticity. With expectations of increased presence and participation of Indigenous people in publicly funded education in Canada consistent with Call 62 in the Calls to Action of the Truth and Reconciliation Commission of Canada (2015), school divisions must confront the need to ensure that Indigenous staff participation is prioritized and that they defer to Indigenous community norms and expectations (Burgess & Cavanagh, 2015; Pidgeon et al., 2013) when considering questions of authentic Indigenous voice and participation. Through the lens of an Indigenous analytical framework and the principles of critical policy analysis (Apple, 2019), we examined the Saskatchewan educational policy environment to explore ways in which extant policy reflects imperatives of Indigenous participation and identity. While our analysis identified shortcomings in these areas, we made sense of these gaps in policy and provided a framework for school divisions useful in prioritizing Indigenous participation at all levels and in beginning to navigate the complex issues associated with Indigenous identity and authenticity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.048 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".