Coaching Extremely Normal Clients in Professional Services Firms: Contemptuous and Compassionate Perspectives from Philosophy and Psychoanalysis
Bibliographic record
Abstract
In this paper, I investigate my experience coaching extremely normal clients in a leading professional services firm. I contextualize my experience with insights from philosophical and psychoanalytic writing on normalcy, contrasting a contemptuous view of normalcy in the writings of Nietzsche and de Boton with a more compassionate understanding of normalcy in the writing of Christopher Bollas. I explore Bollas’ concept of ‘normotic illness,’ which he contrasts with psychotic illness, with the latter representing a loss of objectivity, and the former a loss of subjectivity. I conclude with recommendations on how to coach the overly normal using 360 assessments and follow up coaching that emphasizes the importance of authenticity, self-reflection and individuality in leadership effectiveness. I conclude with a reflection on the differing roles philosophy and psychoanalysis might play more generally in human development processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".