Bibliographic record
Abstract
Purpose The purpose of this paper is to elucidate the implications of relational cultural theory (RCT) for mentoring individuals who have enacted moral courage. Design/methodology/approach Overviews of the construct of moral courage, the nature of work-related mentoring and RCT are provided. Subsequently, the relevance and implications of RCT for understanding moral courage-related suffering, and for supporting the growth, resilience and vitality of those who have enacted moral courage are discussed. Findings Within RCT, moral courage-related suffering is located in disconnection, invalidation and isolation for which sufferers also feel held at fault. Self-protective behaviors, including disavowal of self, can perpetuate this suffering. Practical implications Five insights from RCT for supporting the growth, resilience and vitality of individuals following acts of moral courage are elaborated, including affirming efforts to activate supportive relationships; demonstrating “radical respect”; facilitating voice; engaging through mutuality and fluid expertise; and, reframing resilience. Social implications The dearth of attention to ways of supporting those who suffer following acts of moral courage reflects previously documented findings about the short-shrift given to issues of human health and sustainability in organizations and organizational research. Implications for policy, practice and education are described. Originality/value This paper extends the RCT perspective in mentoring, and addresses an important gap in the moral courage literature, namely, the identification of a theoretically grounded approach through which to support the growth, resilience and vitality of individuals who have enacted moral courage.
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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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.049 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".