Building Racially Responsive Leadership
Bibliographic record
Abstract
The global Black Lives Matter protests in response to the killing of George Floyd ignited an immense response from Canadian higher educational institutions, who avowed to strengthen their commitment to addressing systemic anti-Black racism. Students and employees urged higher education leaders to acknowledge, redress, and correct the prevailing racial injustices impacting diverse Black communities. This call to action moved Canadian higher education leaders to examine gaps within leadership structures, practices, and policies through a lens of critical race theory (CRT) on how to confront emergent racial realities. Combatting these racial realities requires leaders to build racial literacy skills and develop equitable organizational strategies. This Organizational Improvement Plan (OIP) presents a possible solution that bolsters the capacity of higher education leaders in a metropolitan college in Canada to advance racial equity across leadership structures by making race salient in institutional goals, adopting anti-racist approaches, and building racially responsive behaviours towards realizing organizational equity outcomes. Racially responsive leadership (RRL; Harper, 2017) is a proactive leadership approach that authentically and with intentionality builds racially just environments to heighten institutional accountability, with a goal to improve the lives, experiences, and outcomes of diverse Black communities. This OIP incorporates principles of RRL and transformative leadership to realize deep change through creating new knowledge on equity leadership practices that develop multiple layers of accountability. Through building racial literacy among leaders, inspiring collective action to construct equitable leadership structures, this OIP is focused on how leaders can implement organizational equity outcomes to address the perils of systemic anti-Black racism.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".