Mindfulness and Complex Identities in Equity Training: A Pilot Study
Bibliographic record
Abstract
Abstract Equity (diversity, anti-oppression) training refers to teaching self-awareness of social location, privilege/disadvantage that mainstream culture perpetuates, and working self-reflectively for equity in everyday interactions amid diversity. This paper investigates the efficacy of equity training on the basis of mindfulness, compassion, and an intersectional/complex identity. Mindfulness refers to a vivid awareness of the present moment, of one’s own embodiment, thoughts, and emotions, fostering self-awareness and compassion. A complex conception of identity in an intersectional approach recognizes that people hold multiple statuses and I propose that this helps them self-identify with both privilege and disadvantage beyond dichotomic notions, finger pointing, and angry reactions. I first discuss the relevant theory of intersectionality and compassion, and consider the significance of this kind of approach to equity training. I then report the outcome of my pilot study that compares the impressions of two small groups of trainees in different equity workshops who were interviewed: The target group attended a workshop based on mindfulness exercises within an intersectional framework, and the comparison group attended a workshop not based on mindfulness, but that included information about privilege, disadvantage, and compassion. The findings point to an enhanced and emotionally involved self-awareness in respondents that participated in the target group workshop, based on mindfulness and empathy enhancing exercises.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".