Change Management, Quality Assurance and Race: Advancing Race-Based Equity in Canadian Higher Education by Leveraging Established Institutional Mechanisms
Bibliographic record
Abstract
Higher education in Canada has been well regarded for decades. Academically rigorous programs and institutions graduate strong students while also attracting top local and global talent as employees to their institutional communities. However, the field of Critical Race Theory, among others, explicates that higher education institutions are not equitable for all bodies. Individuals with intersectionalities inclusive of the Black Diaspora continue to be excluded from academic success and success as employee stakeholders in higher education institutions. Critically, Black bodies continue to struggle with entry to higher education and means to economic success post-graduation, as well as lack of inclusivity to the institutional structure as an employee. Thus, the author of this Organizational Improvement Plan (OIP) presents a discussion of present-day experiences of Black bodies at Postsecondary MV with the intention of challenging anti-Black racism. Through current research and the author’s lived professional experience, the writer demonstrates that Black bodies are frequently the recipients of physical intimidation on campuses, isolation at work, discriminatory hiring practices that block senior leadership roles, barriers to academic success as a student, and additional exclusionary acts. Further, the author recommends a solution focused on changes to quality assurance and continual improvement policy and procedures to improve equity, diversity and inclusion of stakeholders at an institutional level. This change is led through authentic and adaptive leadership practices, and informed by Lewin’s Theory of Planned Change and Humble Inquiry. The change solution presented offers a pervasive yet manageable method to increased stakeholder accountability, engagement, and action towards the change of an ubiquitous monoculture found in Postsecondary MV. The author acknowledges the highly political and sensitive nature of the topic. Thus, the writer recommends advancing anti-Black racism through established practices across the institution to establish shared ownership for the necessary culture change. With intention, the author of the OIP unveils a pragmatic change solution that is manageable for over-burdened employees in higher education while avoiding tokenistic strategies for equity.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.050 | 0.025 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 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".