Cultural Humility and Social Identity in Coaching
Bibliographic record
Abstract
In the evolving field of coaching, social identity has become a crucial yet underexplored element of effective coaching practice. Social identity—encompassing race, gender, age, class, and other characteristics—shapes individual experiences and interactions in profound ways (Tajfel, 1972; Crenshaw, 1991). This paper argues that coaches must move beyond traditional notions of cultural competence to embrace cultural humility (Tervalon & Murray-García, 1998), an ethical stance rooted in self-reflection and openness. Drawing on intersectionality theory (Crenshaw, 1991) and philosophical perspectives on the self and the Other (Sartre, 1943; Beauvoir, 1949), the paper suggests that cultural humility allows coaches to better understand and engage with the complexities of social identity. Two propositions are advanced: (1) social identity is a fundamental factor in coaching relationships, and (2) cultural humility is essential for effective coaching. Concrete tools, including assuming incomplete knowledge, challenging stereotypes and biases, creating resonant relationships, and fostering authenticity and growth, are offered as practical strategies for integrating social identity and cultural humility into coaching. This paper concludes by proposing future research on developing cultural humility as a core coaching competency, highlighting its importance in today’s diverse and globalized professional environments.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".