Bibliographic record
Abstract
This article is derived from my participation in, and experiences at, the fifth annual War Horse Symposium in Alberta in September 2015. The creative arts offer tools for self-awareness and self-care to military personnel, their families, and other first responders. Resilience training engages participants in interpersonal and organizational reconnaissance through the language of theatre – the language of bodies analyzing experiences and stories. This work addresses physical flexibility, energy flow and mindfulness, peer support, action-based role-play, and the things people say to each other while witnessing what can't be put into words. It also describes an environment that activates pleasure, laughter, and play. Four elements promote self-knowledge, community connection, and healing in creative resilience training: witnessing and being witnessed, discovering narrative options for re-storying one's life, experiencing oneself beyond the definition of one's injury, and connecting with others in ways that build healthier brains. When we experience being respected, not shamed; listened to, not ignored; safe, not at risk; our neurological pathways change. Inter-subjectively, both participant and witness become changed neurologically and psychologically. War Horse Awareness Foundation founder Deanna Lennox says, “Creative resiliency training gets connected to their bodies and spirits.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".