Amplifying Silenced Voices: A Critical Reflection on Challenges Facing Occupational Therapy Academics With Multiple Minoritized Identities
Bibliographic record
Abstract
The issues faced by racialized; female; immigrant; and two-spirit, lesbian, gay, bisexual, transgender, queer/questioning, intersex, and asexual/agender + (2SLGBTQIA+) occupational therapy academics and practitioners highlight the overlapping systems of oppression due to their multiple minoritized identities (MMIs). Through critical reflection, the authors bring to light how oppressive occupational therapy structures and processes continue to sustain Othering within the profession, including the paradox of occupational justice. The authors caution that ignoring issues faced by occupational therapy academics with MMIs might end in tragic intersectionality. Positionality Statement: Natasha Smet identifies as an immigrant queer woman of color, scholar, and practitioner who was born and raised in South Africa during the Apartheid era when laws were enforced to segregate people solely on the basis of race. Although Apartheid ended in 1994, her experiential knowledge of systemic racism, overt discrimination, and oppression continued as a survivor of educational and academic workplace violence and abuse in the United States. Her lived experience of oppression continues to be her catalyst to disrupt white supremacy across academic institutional settings. Jeffrey John Andrion is a racialized, straight, cisgender, immigrant male academic who was born and raised in the Philippines. Although he is an immigrant settler of Canada, he is also a descendant of former colonizees in his native home country. With experiential knowledge of racialization and Othering, he grew up with the terms resistance and oppression. In this column, we define Othering as "the process whereby an individual or groups of people attribute negative characteristics to other individuals or groups of people that set them apart as representing that which is opposite to them" (Rohleder, 2014, p. 1306).
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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 | 0.051 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.076 | 0.086 |
| Scholarly communication | 0.035 | 0.029 |
| Open science | 0.012 | 0.034 |
| Research integrity | 0.027 | 0.064 |
| Insufficient payload (model declined to judge) | 0.003 | 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".