Bibliographic record
Abstract
Canada has always presented itself as a nation built on values of fairness and tolerance (Mackey, 2002). Yet across the entire country Aboriginal peoples are severely marginalized. Statistics suggest that the situation in Saskatchewan, one of three Canadian prairie provinces with an enormous landmass and an abundance of natural resources, is perhaps more dire than in any other province. The evidence for this can be seen in the comparatively low secondary school graduation rates (Saskatchewan Ministry of Education, 2010), the extremely high incarceration rates (Gebhard, 2013), and the deep levels of poverty experienced by Saskatchewan’s Aboriginal peoples1 (Douglas Gingrich, 2009; Hunter Douglas, 2006). Approximately 16% of the province’s one million inhabitants claim Aboriginal status as descendants of the Indigenous groups who occupied the area in precontact times (Waiser, 2005). Colonization and the racialization of the province’s Aboriginal population have been instrumental in keeping the majority living far below the poverty line. This chapter focuses on the promise of Treaty Education to help address the social injustice experienced by the province’s Aboriginal peoples. The chapter begins with an overview of the challenges posed by colonization and the government’s reneging on its treaty promises both in the historical context and in present-day Saskatchewan. This discussion provides the context for the main purpose of this chapter, namely, to discuss recent developments around the most promising strategy to help Aboriginal students, and by corollary, Aboriginal peoples in Saskatchewan. This section focuses on Treaty Education, a government policy mandating schools to teach Treaty Education to each student every year from kindergarten to the twelfth grade. It describes a study we conducted at the University of Saskatchewan to use the admissions process into the Teacher Education Program as a way of gaining insight into the collective understanding of the treaties negotiated between the federal government and prairie First Nations in the 1870s. Particularly helpful in this endeavor is the taxonomy of racial discourses described by Frankenberg (1993): essentialist, color-blind, and race cognizance. This study comprises the chapter’s last section and examines the efficacy of 5 years of Saskatchewan’s Treaty Education initiative.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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".