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
Objectives: Through the use of closed-circuit television (CCTV) video footage, the current study builds upon the drug transaction work of Piza and Sytsma by developing a crime script for open-air drug selling. Methods: Researchers conducted a systematic social observation of CCTV footage of open-air drug markets in Newark, NJ. The data were used to identify sequential stages of drug transactions. Fisher’s exact tests measured whether buyer and seller activities during specific acts of the drug transaction event were related to activities seen in subsequent stages. Results: This study finds three distinct acts to open-air drug events. During the pretransaction act, one party (usually the buyer) initiates the transaction. There must then be an exchange of narcotics for money, which typically occurs in one simultaneous transfer and in one location. There is necessarily posttransaction mobility, with sellers most commonly maintaining their anchor point within the drug territory—particularly when the interactions are buyer initiated. Conclusions: Results of this study contribute to the crime script and situational crime prevention literatures by demonstrating acts inherent in public drug selling and by advocating for a focus on the posttransaction period and seller anchor points within drug markets through leveraging the sentinel role of police officers.
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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