Indigenization of Postsecondary Education Applied Learning Curriculum Development
Bibliographic record
Abstract
The Truth and Reconciliation Commission of Canada’s (2015) Calls to Action have awoken Canadian society to the reconciliation. Although there is a growing body of knowledge on the individual topics of Indigenous education, knowledge, and leadership, there is relatively little research bringing together these topics in curriculum development practices in a postsecondary education skilled learning context. My problem of practice (PoP) is one that strives to address a low enrolment of Indigenous adult learners and lower positive outcomes from skilled training programs. Situating this problem from my perspectives as a Canadian-born visible minority Settler on Turtle Island and postsecondary education leader at Prairie Tradespersons Association (a pseudonym), this organizational improvement plan (OIP) presents and analyzes the problem through the lens of Indigenous education, knowledge, and leadership perspectives as both an organizational leadership challenge and an opportunity for reconciliation. The problem also lies at the intersection of social justice and equity, diversity, inclusiveness, and decolonization. Further complicating the problem are its adult education, socioeconomic, and even geographic barriers. After discussing my leadership approaches to change, and the merits of several alternative solutions, I focus on the planning and development required for the chosen solution and the organization’s anticipated future state. Based on linkages between research-based leadership approaches and organizational change theories, the final part of the planning brings together my proposed implementation, communication, and monitoring and evaluation plans, which form my OIP.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".