Why Individuals Choose to Post Incriminating Information on Social Networking Sites: Social Control and Social Disorganization Theories in Context
Bibliographic record
Abstract
Facebook, Twitter, MySpace, and many more social networking sites are becoming mainstream in the lives of numerous individuals in the United States and around the globe. How these sites could potentially impact one’s perception of community, as well as the ability to enhance (or impede) strong social bonding, is an area of concern for many sociologists and criminologists. Current literature is discussed and framed through the lenses of social disorganization and social control theories as they relate to an individual’s propensity to commit crime/indiscretions and then post comments relating to those activities on social networking sites. The result is gained insight into the communal attributes of social networking and a contribution to the discussion of the relationship among the social components of the internet, criminal activity, and one’s sense of community. Implications and areas of future research are also addressed.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".