Bibliographic record
Abstract
Considering past brutalities enacted through policies of assimilation and residential school operations in Canada, Indigenous people have done remarkably well (Stonechild, 2006). However, current quality of life for the majority of Indigenous people in Canada, especially in educational attainment, lags far behind non-Indigenous Canadians, with the largest disparity in Saskatchewan (Findlay, Garcea, Hansen, Antsasen, & Cheng, 2014). The Truth and Reconciliation Commission of Canada (2015) recommended engagement of Indigenous people in educational strategy development. One of University of Saskatchewan’s priorities is a commitment to Indigenous engagement through relationships, programs, and scholarship (University of Saskatchewan, 2018). Although perhaps well-intentioned, Indigenization of the campus to encourage Indigenous student success adopts a pan-Indigenous approach which lumps First Nations, Metis and Inuit people together, despite vast differences between and among groups. This study will investigate opportunities for the University of Saskatchewan to provide programming to support Metis participation and student success. Knowledge of current Metis students and graduates of a teacher education cohort and Metis students across campus will be gathered through narrative accounts and talking circles, for participant-researchers to detail experiences at university during their studies and explore opportunities the university has in place and could create to encourage Metis student success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".