Supporting First Nations and Métis Post-Secondary Students’ Academic Persistence: Insights from a Canadian First Nations-affiliated Institution
Bibliographic record
Abstract
Post-secondary institutions have a critical role to play in addressing the Truth and Reconciliation Commission (TRC) Calls to Action through indigenization strategies (TRC, 2015) but, to date, it has proven challenging. In this study, the research lens was expanded to focus on First Nations-affiliated post-secondary institutions, since these come closest to providing authentic approaches to indigenization. The purpose of this qualitative case study was to explore how social support affects the academic persistence of First Nations and Métis students at a First Nations-affiliated post-secondary institution. The findings revealed that administrative and pedagogical practices, consistent with Indigenous ontologies, enabled students to respond to challenges stemming from the generational effects of colonization, and promoted individual and familial advancement, cultural growth and identity formation, community development, and Indigenous sovereignty. It is concluded that mainstream institutions can benefit from the findings as First Nations-affiliated post-secondary institutions provide valuable understandings of potential transformations toward indigenization.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| 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".