Creating Fertile Voids: The Use of Poetry in Developmental Coaching
Bibliographic record
Abstract
There has been much attention in coaching to the drawing together of models of practice from fields such as education and therapy. However, there has been less attention given to the broader use of the arts, such as poetry. Although humans have used poetry to understand ourselves for millennia, little attention has been paid to how the use of poetry can contribute to coaching, and offer different perspectives on the human condition. We take the view that clients who enter a coaching relationship are looking for opportunities to explore different perspectives for their presenting issues. Poetry has the power to enable creativity, awareness, emotion and empathy. It permits a type of exploration that is often neglected in the milieu of the everyday, thus allowing a different insight and perspective. Using poetry sensitively and creatively allows the client to search for meaning outside of a transactional dialogic approach. This approach is challenging, and the coach is required to be able to work with concepts of the felt sense and uncertainty. This paper starts to explore this approach and offer some ideas of how it can be brought in to practice.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".