Increasing Pathways to Leadership for Black, Indigenous, and other Racially Minoritized Women
Bibliographic record
Abstract
Leadership positions within post-secondary institutions (PSIs) remain elusive to women generally, and to Black, Indigenous, and other racially minoritized women in particular. In this paper, we argue that pathways to leadership, particularly for non-traditional, non-normative and critical approaches that can come from the differently situated epistemic positioning of Black, Indigenous, and other racially minoritized women, are important as beginning steps towards progressively dismantling standardized Eurocentric, androcentric, and corporatized academic workplace cultures. This type of reform is essential preliminary work in the process toward greater equity and inclusivity in academic institutions. Note then that we are writing of a significant amount of substantive change needed to enact crucial initial reform, in tandem with, and beyond which we should continuously push for more radical transformation (Dryden 2022; Patel 2021). As such, we propose initiatives that universities can take to address some of the common gendered, racialized, and class-related exclusions and inequities evident in academic workplaces. This is in acknowledgement that academic institutions, having demonstrated a predilection for the co-optative and performative, are barely able to reform meaningfully, let alone engage the “transformation” and “decolonization” with which reform is often confused and erroneously conflated. Grounded within institutional research, we detail the commitments required from governing bodies, the changes necessary in academic decision-making spaces, the need for timely and transparent data collection infrastructure, and other institutional changes required to enhance the recruitment, hiring, and retention of Black, Indigenous, and other racially minoritized faculty and academic leaders. Together, these practices constitute preliminary reform necessary to create opportunity for more meaningful practices of inclusion.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".