Walk Steady, Keep Going: Navigational Moves of Empowerment, Resistance, and Sustenance in the Academy
Bibliographic record
Abstract
Demographic shifts and increasing diversity have increased calls for more Black women in higher education teaching and leadership in Canada. This chapter examines how I navigate my practical and theoretical journey in academe through my ontological experiences as a Black female immigrant in large university in Southern Ontario Canada. Drawing on critical race theory (CRT) as a theoretical frame, I explore and theorize my resistance to racial microaggressions through what I describe as “navigational moves.” These “navigational moves” include decolonizing education, spirituality, self-care, and developing a supportive network. These “navigational moves” are grounded in my history and experiences. The chapter explores notions of resistance, empowerment, and sustenance as important factors in challenging racial microaggressions in academe.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.033 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".