Gambling in the Landscape of Adversity in Youth: Reflections from Men Who Live with Poverty and Homelessness
Bibliographic record
Abstract
Most of the research on gambling behaviour among youth has been quantitative and focused on measuring prevalence. As a result, little is known about the contextual experiences of youth gambling, particularly among those most vulnerable. In this paper, we explore the previous experiences of youth gambling in a sample of adult men experiencing housing instability and problem gambling. We present findings from a qualitative study on problem gambling and housing instability conducted in Toronto, Canada. Thirty men with histories of problem or pathological gambling and housing instability or homelessness were interviewed. Two thirds of these men reported that they began gambling in youth. Five representative cases were selected and the main themes discussed. We found that gambling began in early life while the men, as youth, were also experiencing adversity (e.g., physical, emotional and/or sexual abuse, neglect, housing instability, homelessness, substance addiction and poverty). Men reported they had access to gambling activity through their family and wider networks of school, community and the streets. Gambling provided a way to gain acceptance, escape from emotional pain, and/or earn money. For these men problematic gambling behaviour that began in youth, continued into adulthood.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".