Exploring Places of Street Drug Dealing in a Downtown Area in Brazil: An Analysis of the Reliability of Google Street View in International Criminological Research
Bibliographic record
Abstract
This study assesses the reliability of Google Street View (GSV) in auditing environmental features that help create hotbeds of drug dealing in Belo Horizonte, one of Brazil’s largest cities. Based on concepts of “crime generators” and “crime enablers,” a set of 40 items were selected using arrest data related to drug activities for the period between 2007 and 2011. These items served to develop a GSV data collection instrument used to observe features of 135 street segments that were identified as drug dealing hot spots in downtown Belo Horizonte. The study employs an intra-class correlation (ICC) statistics as a measure of reliability. The study showed mixed findings regarding agreement on some features among raters. One on hand, the observer’s lack of familiarity with the local culture and street dynamics may pose a challenge with regards to identifying environmental features. On the other hand, factors such as image quality, objects that obstruct the view, and the overlooking of addresses that are not officially registered also decrease the reliability of the instrument. We conclude that a combination of tools and strategies should be applied to make the use of GSV truly reliable in the field of international criminological research.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".