Falling Through the Cracks
Bibliographic record
Abstract
Low-income preseniors represent a vulnerable, often overlooked population facing multiple challenges related to finding and sustaining employment, limited financial resources, mental and physical health challenges, mobility issues, and ineligibility for pensions and benefits for seniors. These issues make finding suitable, affordable housing particularly challenging when compounded with limited affordable housing stock, thus increasing this population’s risk for housing insecurity/homelessness. This qualitative, exploratory study examined subsidized housing issues for low-income preseniors from the perspective of subsidized housing providers ( n = 16). Barriers for this population occurred within individual (limited financial resources; complex health, mental health, and disability issues; current unsafe/inadequate housing; and new immigrant status) and structural (strict age cutoffs, inadequate safe/affordable housing supply, lack of information about the housing and service needs of the population, and ineffective collaboration within the sector) domains. Policy changes at the provincial and federal levels related to income support, availability of affordable housing supports, and immigration are recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.005 |
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".