Bibliographic record
Abstract
Collective trauma must be transformed in and by the community. Since mutual social support forms the foundation for psychologically rebuilding community, relationships with family, co-workers, and neighbours become a primary source of recovery and healing. This allows communities not only to bounce back, but to “bounce forward” as Manyena et al. (2011) have indicated in the title of their article and move from a sense of powerlessness towards pride and deep-rooted attachment. Most communities have outlined plans to deal with natural disasters and emergencies, but municipal officials and community leaders have little guidance on how to address psychological or emotional community trauma and its aftermath. When trauma strikes, it affects everyone. It is not uncommon for residents to feel less positive, less energetic, and unable to enjoy life in the aftershock of a trauma; they are less able to generate and implement healing strategies themselves. Therefore, it is imperative that strategies for local community healing be identified prior to the occurrence of trauma. The project I discuss in this chapter created an opportunity for community members to engage in just such strategies. Using principles of research-based theatre (Belliveau, 2015) I created a readers’ theatre presentation about the murder of a family in a close-knit small town. I theatricalised research data gleaned from three case studies of community trauma in an urban community (two domestic homicides), a rural one (the murder of a young girl), and a school community (a mass shooting). This performance functioned as a starting point for discussions on how to promote community healing effectively outside of an existing real-life trauma and its emotional and psychological wake.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.074 | 0.022 |
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".