Drilling Down: An Examination of the Boom-Crime Relationship in Resource Based Boom Counties
Bibliographic record
Abstract
Objective: to examine the boom-crime relationship in resource-based boom counties, and to propose socio-economic and legal measures to reduce the boomtown effect.Methods: dialectical approach to cognition of social phenomena, allowing to analyze them in historical development and functioning in the context of the totality of objective and subjective factors that determined the choice of the following research methods: formal-logical, comparative-legal, survey, interview, focus groups, generalized least squares method.Results: The expansion in natural resource development in rural communities has led to a number of social problems in these places. The media, community stakeholders, as well as law enforcement and human service personnel have reported that the rapid growth in these communities leads to increased crime and other social ills. In order to better understand the boom-crime relationship, index crimes in oil and natural gas producing counties in Montana and North Dakota were examined. Comparison of 2012 crime rates in a matched sample of counties revealed that crime rates were higher in oil-impacted counties. A pre-post analysis found that violent crime in boom counties increased 18.5% between 2006 and 2012 while decreasing25.6% in a matched sample of counties that had no oil or gas production. Inconsistent with the media portrayal of these communities as a new "wild west" we did not find a significant association between oil or natural gas production and property or violent crime in a series of OLS regression models. Scientific novelty: for the first time the article uses index crimes in oil and natural gas producing counties in Montana and North Dakota to reveal the association between the rapid growth of towns and the crime rates.Practical significance: the main provisions and conclusions of the article can be used in research and educational activity, as well as for predicting the social-economic development of boomtowns.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".