Leveraging Community-University Partnerships to Develop a Strength-Based and Individualized Approach to Humanizing Housing Service Delivery for Individuals with Fetal Alcohol Spectrum Disorder (FASD)
Bibliographic record
Abstract
This field report summarizes and advances key learnings for leveraging community–university partnerships addressing housing service gaps for high-risk, marginalized populations with complex needs. We describe our navigation of existing and forged intersections to develop a strength-based and individualized approach to humanizing housing service delivery for individuals with fetal alcohol spectrum disorder (FASD). Our account is framed by four questions: why community and university partners came together to develop a responsive approach through the CanFASD network; who became key stakeholders in the partnership; how our humanizing housing approach is guiding the navigation of complexities inherent in service delivery for individuals with FASD; and what insights about creating intersections are we applying to our community-university partnerships.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.034 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".