Leading with Compassion: Co-designing a Workshop That Responds to a Report of Sexual Harassment or Discrimination with Unbiased Compassion
Bibliographic record
Abstract
Abstract Sexual harassment and discrimination are continuing and chronic workplace problems (Quick & McFadyen, 2017) that affect the health, well-being and socio-economic future of victim/survivors (Blau & Winkler, 2018). Despite this, management and leadership education have been primarily addressing this workplace issue from a legal responsibility perspective and using preventative strategies such as promoting the value of equity, diversity, inclusion and belongingness and explaining the importance of safe, healthy and respectful workplaces. While the establishment of policies, human rights training and disciplinary procedures are undeniably important, rarely do business educators prepare future managers to engage with employees in trauma-informed, compassionate and respectful ways. The co-authors have used a collective restorying process to engage in co-designing a workshop for early career managers and students of management and leadership. The workshop includes iterative exploration of the language and authentic performativity of unbiased compassion while engaging in collective reflexivity. The basis of the workshop centres the research proposition that to support a claimant the manager must performatively lead with authentic compassion while using unbiased language in order to assure procedural justice while mitigating procedural trauma. Early career managers, and hence their organizations, are ill-equipped to deal with workplace investigations of sexual harassment and discrimination. By collectively exploring and practicing unbiased compassion, managers will not only be more prepared to respond to a claim of sexual harassment or discrimination, but they will also reduce employee's felt sense of procedural trauma and increase the organization's likelihood of due diligence.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".