More Than a Checklist: Meaningful Indigenous Inclusion in Higher Education
Bibliographic record
Abstract
Since the 1970s there has been increased focus by institutions, government, and Indigenous nations on improving Aboriginal peoples participation and success in Canadian higher education; however disparity continues to be evident in national statistics of educational attainment, social determinants of health, and socio-economic status of Aboriginal compared to non-Aboriginal Canadians. For instance, post-secondary attainment for Aboriginal peoples is still only 8% compared to 20% of the rest of Canada (Statistics Canada, 2008, 2013). A challenge within higher education has been creating the space within predominately Euro-Western defined and ascribed structures, academic disciplines, policies, and practices to create meaningful spaces for Indigenous peoples. Indigenization is a movement centering Indigenous knowledges and ways of being within the academy, in essence transforming institutional initiatives, such as policy, curricular and co-curricular programs, and practices to support Indigenous success and empowerment. Drawing on research projects that span the last 10 years, this article celebrates the pockets of success within institutions and identifies areas of challenge to Indigenization that moves away from the tokenized checklist response, that merely tolerates Indigenous knowledge(s), to one where Indigenous knowledge(s) are embraced as part of the institutional fabric.
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 | 0.039 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.021 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".