Hakomi in Action: A Narrative
Bibliographic record
Abstract
This article introduces readers to Hakomi in an alternative manner. Rather than present an overview of the interventions associated with the principles, it provides a personal narrative illustrating how the principles are demonstrated, and the effects of this implementation on the counselling process. To begin, the article provides a context of my therapy experience by introducing my therapist and my reason for seeking Hakomi therapy. Thereafter, I discuss each principle with reference to my own therapy experiences of it, and link to the therapeutic alliance throughout. Finally, I reflect on the experience of writing this narrative and provide a summary of applied Hakomi principles. Context Attending Hakomi weekend workshops intrigued me regarding the method of work I saw and experienced the trainer doing. Hakomi tapped into a part of me that I could not explain or figure out and triggered a level of emotion new to me. Out of curiosity (and what I now know as a tapped inner drive towards healing) I began attending individual therapy sessions with a highly recommended Hakomi therapist, whose name is Anna. When I first went to see her, I was unsure what to expect. I had previously tried many cognitive and behavioural methods to heal myself, but something was still missing. The following is an experiential narrative description of the principles of Hakomi in terms of how they showed up in my therapy. Each principle will be reviewed with a description of how each was experienced. Mindfulness
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".