In and out of Aboriginal gang life : perspectives of Aboriginal ex-gang members
Bibliographic record
Abstract
This research project generated a categorical scheme to describe the facilitation of gang entry and exit for Aboriginal ex-gang members using the Critical Incident Technique (Flanagan, 1954; Woolsey, 1986) as a method of qualitative data analysis. Former gang members responded to the questions: (a) What facilitated gang entry for you? (b) What facilitated gang exit for you? Participants provided 103 and 136 critical incidents which were categorized into two separate category schemes each containing 13 different categories. The 13 categories for gang entry were; engaging in physical violence, proving one’s worth, hanging around delinquent activity, family involved in gangs and following a family pattern; going to prison, gang becoming family and support system, looking up to gang members and admiring gang lifestyle, becoming dependant on gang, experiencing unsafe or unsupportive parenting practices, gaining respect by rank increase, reacting to authority, caught in a cycle of fear, and partying. The 13 categories for gang exit were; working in the legal workforce, accepting support from family or girlfriend, helping others stay out of or move away from gang life, not wanting to go back to jail, accepting responsibility for family, accepting guidance and protection, participating in ceremony, avoiding alcohol, publically expressing that you are out of the gang, wanting legitimate relationships outside gang life, experiencing a native brotherhood, stopping self from reacting like a gangster, and acknowledging the drawbacks of gang violence. Diverse methods of checking trustworthiness and credibility were applied to these category schemes, and it was found that both category schemes can be used confidently.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".