Lived Experience as Evidence for Research, Policy, and Advocacy on Homelessness in Canada
Bibliographic record
Abstract
Abstract The rise of evidence‐based policymaking highlights the importance of evidence and how it is defined and utilized. It also uncovers the gaps in evidence and where lived experience stands to benefit policymaking. The rising level of encampments, homelessness, and housing unaffordability in Canada emphasize the need to consider how and where lived experiences of homelessness are included. This article interrogates the inclusion of lived experiences of homelessness in three specific domains: research, policy, and advocacy. Our findings from systematic reviews of each area reveal an evidence‐policy gap in homelessness policy and governance. Across all three domains, scholars and practitioners alike often fail to explicitly include lived experience to inform their efforts. Enhanced coordination and policy learning across and between the domains can offer a pathway to measurably improve policy effectiveness.
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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 | 0.038 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.011 |
| 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".