The Relationship between Social Capital and Weapon Possession on Campus.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present research focused on the problem of how college officials might be able to predict weapon possession on college campuses. We hypothesized that measures of social capital (i.e., trust and participation in society) may be useful in identifying individuals who are likely to possess weapons on campuses. Prior research has shown that those who report both relatively low levels of trust in society and high levels of participation in society engage in higher levels of risk-taking than others. The study utilized an online survey method involving 531 college students. The results support the conclusion that colleges may be able to use measures of social capital to predict weapon possession on college campuses. ********** Violence on school campuses has become an increasing concern among educators, parents, and students (Furlong & Morrison, 2000). Data from 1996-1997 indicated that there were 11,000 incidents of violence involving weapons in public schools (Heaviside et al., 1998). Without a doubt, the Columbine and Virginia Tech shootings are still painful memories. Other incidents of campus violence have not grabbed the national spotlight. From 2000 to 2008, institutions of higher education experienced 83 incidents of lethal violence that involved weapons (Drysdale, Modzeleski, & Simons, 2010). Prior research has shown that perpetrators of school violence have common characteristics, including social alienation and accessibility to guns (Bender, Shubert, & McLaughlin, 2001). In the present paper, we investigated the hypothesis that there is a link between the extent to which individuals trust and participate in society and the degree to which students engage in risky behaviors, including possessing weapons. The term social capital has been described as the extent to which one cooperates with other within a group (Fukuyama, 1999; Putnam, 1993). Numerous studies have shown that there is a link between the social capital of large populations and a variety of health-related measures (Kawachi, Kennedy, Lochner, & Prothrow-Stith, 1997; De Silva, McKenzie, Harpham, & Huttly, 2005; Crosby et al., 2003). For example, Kawachi, Kennedy, Lochner, and Prothrow-Stith (1997) analyzed data from 39 U.S. states, obtained from the nationally representative General Social Survey (GSS: Davis & Smith, 1993). Trust was assessed using three questions: one regarding lack of fairness (i.e., someone will try to exploit you rather than treat you fairly), one regarding social mistrust (i.e., that people are not able to be trusted rather than being trustworthy), and perceived helpfulness (i.e., people will generally help others rather than being exclusively concerned with themselves). Participation in society was defined by the number of social, community, or group organizations to which participants belonged. Results indicated that lower social capital resulting from a decrease in social cohesion was strongly correlated with discrepancy in income, and was also associated with mortality, including death from coronary heart disease. Other studies have found a link between social capital and mental illness (see De Silva, McKenzie, Harpham, & Huttly, 2005). Lindstrom and colleagues found that individuals in Sweden who report low levels of trust in society and high levels of participation report the lowest levels of self-rated health (Lindstrom, 2004b; Lindstrom & Mohseni, 2009) and also the highest levels of risky behaviors, including smoking tobacco (Lindstrom, 2003; Lindstrom, 2009; Lindstrom & Ostergren, 2001), marijuana usage (Lindstrom, 2004a), anxiolytic--hypnotic drug use (Johnell, Lindstrom, Melander, Sundquist, Eriksson, & Merlo 2006), and high alcohol consumption (Lindstrom, 2005). Boyce, Davies, Gallupe, & Shelley (2008) analyzed data from Canadian adolescents and found that low social capital was related to high levels of risky behaviors such as smoking and alcohol use. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it