Seniors and gambling : exploring the issues : technical report
Bibliographic record
Abstract
Seniors constitute one of the fastest growing population groups in North America. One of the major life changes experienced by seniors is retirement. Retirement has two primary implications for seniors: a decrease in income and an increase in leisure time. On average, Canadian seniors have 7.8 hours of free time per day (Statistics Canada, 1994). How they spend that time is of social and economic importance. While many seniors have lived the majority of their lives in a society that has treated gambling activities conservatively, today gambling is legalized, accepted, and mainstream entertainment. Some, such as the Council on Compulsive Gambling of New Jersey (1997) suggest that as high as 5 percent of seniors who gamble are compulsive gamblers. However, there is not a substantial base of research explaining the relationship of increased leisure time to seniors gambling or the extent to which seniors are at risk of becoming addicted to gambling. To better understand seniors and gambling, the Alberta Alcohol and Drug Abuse Commission (AADAC) contracted Howard Research to conduct a two-phase research study to explore 1. What are the gambling attitudes and behaviours of seniors? 2. What prevention and intervention strategies are most effective for seniors? 3. How universal among Alberta seniors are the answers to questions one and two?
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".