Exploring the impact of a culturally restorative post-secondary education program on Aboriginal adult learners: The Urban Circle Training Centre model
Bibliographic record
Abstract
The educational attainment gap between Canada’s Aboriginal and non-Aboriginal peoples both reflects and perpetuates a parallel disparity in socioeconomic conditions. Aboriginal peoples’ distrust of and disengagement from educational systems can be linked to the history of their relationship with the settler state. Therefore, decolonizing education may be one way to address the education gap. This qualitative study of ten Aboriginal graduates from one of Urban Circle’s post-secondary programs explored graduates’ perceptions of the integration of Aboriginal cultural context and content in their program and the effect of these experiences on program completion. Responses revealed five main themes: 1) the cultural context of Urban Circle restored Aboriginal identity; 2) supportive relationships were important to graduates’ success in the FSW/FASD program; 3) the Life Skills course facilitated personal growth, employment readiness and program success; 4) the cultural context of Urban Circle has influenced graduates’ professional work; and 5) Urban Circle had positive influences on graduates’ personal lives. The findings indicate that the cultural content and context at Urban Circle positively impacted student’s educational experience and contributed to their completion of their program.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".