Social influences among drug users and mean field approximation of cellular automata
Bibliographic record
Abstract
Street-involved youth have a propensity for illicit drug use and trade. There are approximately one million street-involved youth in the United States and about 150,000 in Canada. The need for a proper intervention and follow-up strategy seems remarkably clear. A stochastic cellular automata (CA) model of the influences among current and potential illegal drug users and traffickers is presented. Simulation and mean field analysis are used to study the model. The mean field approximation (MFA) and compartmental representation of the model are also studied. The phase plane of the mean field and possible bifurcations of the system are explored. MFA typically provides a good picture of a CA model near a bifurcation. The model allows us to compare the potential effectiveness of different types of responses to the drug epidemic. Both indirect and direct strategies are found effective on their own, but combined strategies proved to be most effective.
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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 | 0.000 | 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 | 0.000 | 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 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".