Pushing against the Margins: Indigenous Theorizing of “Success” and Retention in Higher Education
Bibliographic record
Abstract
What does it mean to be “successful” in higher education? For some in mainstream society, the value is placed on the financial status gained from a university education. Governments and university administration measure success through graduation rates. While the economic and social benefits of a university education are also important to Aboriginal people, successful negotiation of mainstream higher education also entails maintaining their cultural integrity (Tierney & Jun, 2001). Broadening notions of success and corresponding retention theories is important to move forward the agenda of Aboriginal higher education. The purpose of this article is to further the theoretical and practical discussions of educational success of Aboriginal students. Using social reproduction theory and a post-colonial framework, this article presents an argument that shows how/why conventional discourses on retention and student success often exclude Indigenous understandings and worldviews. To this end, it provides a counter-hegemonic on current discourses relating to retention and Aboriginal persistence in mainstream institutions. The article concludes with some thoughts on how to enrich the educational experiences of Aboriginal students from an Indigenous understanding of success and retention.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.055 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".