The EThIC Model of Virtue-Based Allyship Development: A New Approach to Equity and Inclusion in Organizations
Bibliographic record
Abstract
As organizations take on grand challenges in gender equality, anti-racism, LGBTQ+ protections and workplace inclusion, many well-intentioned individuals from dominant groups (e.g., cisgender men, Caucasian, heterosexual) are stepping forward as allies toward underrepresented or marginalized group members (e.g., cisgender women, People of Color, LGBTQ+ identified employees). Past research and guidance assume an inevitable need for external motivation, reflected in the ‘business case’ for diversity and in top-down policies to drive equity and inclusion efforts. This qualitative study explored internal motivations in the form of morally motivated virtues of 25 peer-nominated exemplary allies serving in leadership positions. In-depth life/career story interviews were used to identify the virtues that supported their allyship journeys. Findings demonstrated that they tapped into several virtues that served distinct functions in a 4-stage allyship development process: Stage 1—Energizing psychological investment (compassion, fairness); Stage 2—Thinking through allyship-relevant complexities (intellectual humility, perspective-taking, wisdom); Stage 3—Initiating action (prudence, moral courage, honesty); Stage 4—Committing to allyship (perseverance, patience). We call this the ‘EThIC model of virtue-based allyship development.’ This study has implications for theory and research on a virtue-based approach to diversity, equity and 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".