Admission Policy Review: Strengthening Indigenous In-Community Training Programs
Bibliographic record
Abstract
Canada’s colonial past significantly impacts prospective Indigenous student postsecondary enrollment. For the past fifty years, postsecondary institutions have focused on assimilation and cultural renewal. One assumption is Indigenous learners share similar educational experiences including ease and access to westernized high school programs with a credit or term system and ease and access to transcripts and criminal records checks often required for postsecondary admission. This Organizational Improvement Plan (OIP) addresses the Problem of Practice (PoP) in admission procedures that do not consider Indigenous knowledges, experiences, and criteria for entry into postsecondary programming in SMH Department at LAC College. As an academic manager in SMH Department and facilitator of college career programs in Indigenous communities in central Canada, I explore the organizational context at LAC College and propose a solution to the PoP. This OIP includes a review of LAC College’s admission policy and implementation of an Indigenized admission process. Adaptive and distributed leadership perspectives are the approaches utilized in this OIP. The Critical Paradigm is the underlining perspective, and the voices of Indigenous colleagues and educational partners inform my perspectives in this OIP. I will conclude by discussing the Hiatt 2013 ADKAR change theory and evaluation plan utilized in this OIP.\nKeywords: Utilization Focused Evaluation, Critical Paradigm, Distributed Leadership, Ethical Leadership, Truth and Reconciliation, Indigenous admission criteria, In-Community Training.
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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 | 0.112 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".