Clinical Sociology and Community Interventions
Bibliographic record
Abstract
At least 235,000 people experience homelessness in Canada each year, with over 35,000 experiencing homelessness on any given night (Gaetz et al. 2013a). For many, maintaining housing is challenging due to the absence of essential life skills. This paper departs from a community-identified problem with conventional life skills programming and uses sociological tools to address it. Community partners have expressed a need for a life skills curriculum that is inclusive, representing the intersecting needs and experiences of a diversity of clients, and that will address budgetary constraints of not-for-profit (NFP) organizations in the region. The Community-Ideas Factory: The Life Skills Project consists of an interdisciplinary research team and 16 NFPs collaborating to build a comprehensive, inclusive, relevant, and effective online life skills intervention. Adopting a clinical sociological and community-engaged research approach, our findings emphasize the importance of recognizing that essential life skills are diverse and shaped by the larger social, political, and economic context, such as social inequities. Notably, social justice is identified as a crucial life skill, uncovering the intersectionalities that shape individuals' lives and that must be integrated into life skills programming. This ground-breaking finding is facilitated by our methodology, deviating from the positivist research approaches prevalent in life skills studies. Significantly, the entire life skills curriculum is Equity, Diversity, and Inclusion (EDI)-informed. The intervention addresses immediate financial strains for partner organizations. We anticipate that the intervention will interrupt current cycles of homelessness while holding promise as a preventative measure.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.138 | 0.013 |
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".