“When I think about my future, I just see darkness”: How youth exiting homelessness navigate the hazy, liminal space between socioeconomic exclusion and inclusion
Bibliographic record
Abstract
OBJECTIVES: The overarching objective of this mixed methods longitudinal study was to understand whether and how rent subsidies and mentorship influenced socioeconomic inclusion outcomes for youth exiting homelessness. The focus of this paper is on the qualitative objectives, which evolved from a primary focus on exploring how study mentorship was working as a facilitator of socioeconomic inclusion to focusing on how participants navigated the hazy, liminal space between socioeconomic exclusion and inclusion. METHODS: This was a convergent mixed methods study scaffolded by community-based participatory action axiology. The quantitative component is reported elsewhere and involved a 2-year pilot randomized controlled trial where 24 participants received rent subsidies and 13 were randomly assigned a study mentor; proxy indicators of socioeconomic inclusion were measured every 6 months for 2.5 years. Qualitative objectives were explored using a qualitative descriptive design and theoretically framed using critical social theory. The lead author interviewed 12 participants every 6 months for 2.5 years. Qualitative interviews were analyzed using reflexive thematic analysis with an emphasis on critical interpretation. RESULTS: Navigating the liminal space between socioeconomic exclusion and inclusion was complex and non-linear, and the way youth navigated that journey was more strongly associated with factors like informal mentorship (naturally occurring "coach-like" mentorship) and identity capital (sense of purpose, control, self-efficacy, and self-esteem), rather than whether or not they were assigned a formal study mentor. CONCLUSION: A holistic approach integrating coaching and attention to identity capital alongside economic supports may be key to helping youth exiting homelessness achieve socioeconomic inclusion.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".