We can’t access invisible services: A qualitative study on the visibility of homelessness services in Toronto
Bibliographic record
Abstract
Policy and service visibility are key characteristics to ensuring the accessibility and uptake of government supports. If target populations are unaware of a service or cannot identify it in the complicated landscape, then it is rendered invisible. In a complex policy space, like homelessness in Canada, the visibility of policy is particularly prudent and for many a question of survival. This article introduces findings from a qualitative research study in Toronto where we conducted focus groups with 31 participants accessing homelessness services. Our project investigates to investigate how visible homelessness services are to those experiencing homelessness, and how information about services is shared. Our findings highlight the invisibility of services in the homelessness landscape and the importance of visibility to access. Participants offered recommendations around how best to share information and spoke about the need for more visibility throughout the homelessness delivery system. To avoid continued policy failures in addressing Canadian homelessness, there is a need to ensure the invisible is rendered visible, and for the fragmented services to be accessible to those that need them most.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.019 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".