Research reveals ... : an update on gambling research in Alberta, Vol 4, 2004-2005
Bibliographic record
Abstract
Gambling is a big thing in prison.That's most guys' hustle [way of making money].Some guys draw, some do tattoos, a lot of guys were building log cabins out of popsicle sticks.They have their hobbies.Every guy has a hustle-some guys wash clothes, some sew, shit like that.Gambling increases when they [Department of Corrections] cut hobbies.-Excerpt from Dr. DJ Williams' interview with "Marvin", a 49-year old who had spent nearly a decade in prisonGambling as a leisure activity reportedly runs rampant in prisons.Anecdotal evidence suggests that inmates will gamble on virtually anything that affords an element of skill, luck or risk-particularly card games, professional athletic competitions, dominoes, or other such events to which odds can be assigned.Gamblingrelated debts are reportedly settled between prisoners using items such as soap, hygiene products, cigarettes, currency and anything else of value.This passion for gambling amongst inmates first aroused the curiosity of Dr. DJ Williams while he was working as a forensic psychotherapist in an aftercare program for offenders.After completing his B.Sc. (Psychology) from Weber State and M.Sc.(Exercise & Sports Psychology) from the University of Utah, Williams obtained employment as a counselor in forensics working at a community corrections halfway house.It was there that he initially became aware of how gambling had consumed the lives of many of his offender clients while in prison.It was also at this time that Williams' own career took a drastic turn when he returned to school to complete a Master's of Social Work specializing in forensics.Subsequently, Williams was accepted into the Ph.D. program at the University of Alberta where he studied crime through the lens of sport science, recreation, and leisure.His doctoral thesis was titled Release from the 'us versus them' prison: Granting freedom by giving voice to multiple identities in physical activity and offender rehabilitation.It examined how health and leisure opportunities in prisons impact offender rehabilitation.Now a post-doctoral research fellow with the Department of Physical Education and Recreation, University of Alberta, Williams is presently pursuing an Institute-funded project which is contributing to the scant research available on gambling and gambling behaviours in prisons.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".