Conversational Narratives at Quixote House: How Released Offenders and Religious Members Build Community and Find a New Identity in Winnipeg
Bibliographic record
Abstract
One of the most worrisome situations in current societies is the failure of their correctional system. Even though jails and imprisonment institutions, at least in developed countries, do not have the shameful conditions which characterized them in the past, the high rate of recidivism shows that the correctional function that morally justifies their existence, with its big budget, has not been successful. Individuals that enter into the correctional system barely escape from it during life. However, there is a house in Winnipeg that is making a difference. This essay is about this house, Quixote House, named after Don Miguel de Cervantes’ novel hero, and my engagement to build community in it through conversational narratives. Also this essay shows how conversational narrative plays a role in healing trauma and building a community through which released offenders can find a new identity. For this purpose, it is necessary to set first a theoretical context, addressing the situation of recidivism and parole releases and the efforts to reinsert former offenders into society, which entail many challenges such as clean and affordable housing. Then, there is an explanation of how storytelling addressing trauma and community building, the importance of emotions in this kind of narrative, and the possibility of storytelling in ordinary life, especially in finding personal identity. Following Lonergan’s approach, there is a description about Quixote House and my engagement as priest but also as a another member of the community in which parolees can find a new identity.
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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 | 0.002 | 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 | 0.024 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".