Community-driven approaches to homelessness enumeration: insights from the 2024 Point-in-Time (PiT) Count in Thunder Bay, Canada
Bibliographic record
Abstract
Introduction The 2024 Thunder Bay Point-in-Time (PiT) Count represents an innovative, community-led approach to enumerating people experiencing homelessness (PEH) in a mid-sized Canadian city, co-led by the Lakehead Social Planning Council and the Thunder Bay Indigenous Friendship Centre. Grounded in cultural inclusivity, the initiative sought to enhance PEH enumeration through locally adapted methodologies.Methods This case study incorporated a narrative literature review, structured interviews with key stakeholders, and analysis of data collected from multiple survey sites. A comparative analysis was also conducted, examining enumeration practices in the United States (U.S.) and the United Kingdom (U.K.).Results The Count achieved high participation by incorporating Indigenous languages, cultural protocols, and a co-designed survey process. A custom electronic platform facilitated secure data entry; however, some technical challenges necessitated hybrid solutions. Compared with models used in the U.S. and U.K., the Thunder Bay approach emphasized flexibility, equity, and local relevance, particularly for Indigenous populations.Conclusions Aligning federal mandates with regional priorities can produce more inclusive and actionable data. The Thunder Bay model highlights the importance of co-leadership, digital infrastructure, and culturally grounded practices in enhancing the quality and relevance of future PiT Counts.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".