Challenge for Rehabilitation Counselors: Serving Individuals with Disabilities Involved in Gang Activity
Bibliographic record
Abstract
When street gang was first thought to be a major issue of concern within United States, issue was considered relevant only to major, urban environments (Hauck & Peterke, 2010; U.S. Attorney General, 2008). More recently, however, attention has been directed toward spread of gang to suburban and rural regions (Hauck & Peterke, 2010; Osgood & Chambers, 2000; U.S. Attorney General, 2008). As reported by National Gang Center (2009), distribution of gangs nationwide was as follows: 44.4% in larger cities; 29.1% in smaller cities; 21.4% in suburban counties; and 5.4% in rural counties. Further exploration into ethnicity of individuals in gangs shows that in 2008, 50.2% were Hispanic or Latino; 31.8% were Black or African American; 10.5% were White; and 7.6% identified as (National Gang Center). This serves as a reminder that no areas or ethnicities are immune to impact of gang violence. In fact, research illustrates increasing gang problem in United States (Burch & Chemers, 1997; National Gang Intelligence Center, 2011; Okamoto, 2001). For example, in a survey conducted by Office of Juvenile Justice and Delinquency Prevention, 49% of law enforcement agencies responded that gang activity in their jurisdiction was getting worse (Burch & Chemers, 1997). A similar perception was also noted more recently in 2011 Gang Threat Assessment. The assessment reported a sizable increase in gang membership and collaboration with other gangs or criminal enterprises to conduct larger scale criminal activities (National Gang Intelligence Center, 2011). Specifically, Federal Bureau of Investigation (2011) estimates that there are approximately 33,000 gangs, encompassing 1.4 million active members in United States. Furthermore, these gangs are responsible for approximately 48% of violent crimes and 90% of many other crimes, not considered violent (Federal Bureau of Investigation, 2011). Important to remember is that not all violent crime assaults were sustained by random victims of gang violence. Interestingly, Peterson, Taylor, and Esbensen (2004) note that in gangs can come from a variety of sources including other members of same gang in form of beat ins and violations. This is known as intragang conflict (Hunt & Laidler, 2001). For example, a member of a gang who violates rules of their own gang may experience violent retaliation from other members within same gang (Peterson, Taylor, & Esbensen). In other instances, gang rivalry between competing gangs may also result in negative consequences and injuries to members (Egly & Howell, 2011). Intergang conflict, also known as gang-on-gang has been shown to result from defense of gang's reputation, trespassing, retaliation, and gangs defending their identity (Egly & Howell). These violent acts can result in disabling conditions (Cook & Ludwid, 2000). However, literature fails to cover rehabilitation outcomes of gang involved populations in aftermath of injury. Intergang conflict is a form of interpersonal violence. Historically, World Health Organization (2002), provided a widely used comprehensive definition of interpersonal which encompasses inflicted, generally outside home, by an unrelated individual whom victim may or may not know for the intentional use of physical force or power, threatened or actual ... that either results in or has a high likelihood of resulting in injury, death, psychological harm, maldevelopment or deprivation (p. 4). The Canadian Red Cross (2012) elaborates on this definition by stating that encompasses child abuse, family violence, gender-based violence, bullying and harassment, elder abuse, and community such as gang violence (pg. 8). Interpersonal involving gangs has been known to lead to victim's death (National Spinal Cord Injury Statistics Center, 2003; Center for Disease Control and Prevention, 2012). …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".