From Performative to Transformative Allyship in the Academy: A Critical Conversation
Bibliographic record
Abstract
Join us for a thought-provoking, conversational panel discussion as we delve into the complexities of allyship in academia. Since equity, diversity, inclusion, and Indigeneity (EDII) have become de rigueur in academia, we have seen diverse manifestations of EDII practices ranging from authentic support to performative allyship. It is imperative that we understand how to distinguish surface-level efforts from genuine attempts to redress systemic inequities and practices. Some of us identify with equity deserving groups; some of us identify as allies; some of us have adopted a neutral stance. Regardless of our position, we are all being asked to uphold and often prove our allegiance to EDII standards in our research, our teaching, and our service. This critical panel conversation will explore the multifaceted dimensions of allyship -performative and authentic- and the related implications as well as manifestations within the realms of research, teaching and service within academia. To start the conversation, our panelists – two Indigenous scholars and two ally scholars - will share real-life case examples that shed light on both performative allyship pitfalls and successful strategies to make meaningful contributions to the changing academic landscape. Through these narratives, attendees will gain actionable insights to navigate similar challenges. The panelists will engage in a candid discussion about how to identify, avoid, and address performative behaviours, and what are appropriate roles and responsibilities of diverse academic community members in engaging in authentic EDII practices, particularly when power imbalances are involved. Don’t miss this opportunity to be a part of a vital conversation that has the power to shape the future of academia. Our discussion will be informed by the unique perspectives of four panelists including: Dr. Brittany Wenniseríostha Jock who is Kanyen’kehá:ka (Mohawk) brings the perspective of an early career academic working in the field of nutrition, food security and Indigenous health promotion. Dr. Lucie Lévesque who is of mixed French-Canadian and Irish ancestry brings the perspective of a senior academic working in the field of implementation science, health promotion and physical activity. Brittany McBeath who is of mixed Kanyen’kehá:ka (Mohawk) and Scottish ancestry brings the perspective of a graduate student trainee working in the field of Indigenous health promotion, cultural safety and community mobilization for chronic disease prevention. Colin Baillie who is of mixed English, Scottish, and Romanian ancestry brings the perspective of a graduate student trainee working in the field of health promotion and research ethics.
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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 | 0.057 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.064 | 0.051 |
| Scholarly communication | 0.027 | 0.030 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.021 | 0.048 |
| Insufficient payload (model declined to judge) | 0.006 | 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".