Accuracy differences in cannabis retailer information ascertained from webservices and government-maintained state registries across US states legalizing the sale of cannabis in 2019
Bibliographic record
Abstract
Cannabis retailer locations used to investigate geographic cannabis access are frequently ascertained from two sources: 1) webservices which provide locations of cannabis retailers (e.g., Yelp) or 2) government-maintained registries. Characterizing the operating status and location information accuracy of cannabis retailer data sources on a state-by-state level can inform research examining the health implications of cannabis legalization policies. This study ascertained cannabis retailer name and location from webservices and government-maintained registries for 26 states and the District of Columbia legalizing cannabis sales in 2019. Validation subsamples were created using state-level sequential sampling. Phone surveys were conducted by trained researchers for webservice samples (n=790, November 2019 - May 2020) and government-maintained registry (n=859, February - June 2020) to ascertain information about operating status and location. Accuracy was calculated as the percent agreement among subsample and phone survey data. For operating status and location, webservice derived data was 78% (614/790) and 79% (484/611) accurate, whereas government-maintained registry derived data was 76% (657/859) and 95% (622/655) accurate, respectively. Fifty-nine percent (15/27) of states and the District of Columbia had over 80% accuracy for operating status and 48% (13/27) states had over 80% accuracy for location information with both data sources. However, government-maintained registry derived information was more accurate in 33% (9/27) states for operating status and 41% (11/27) states for location information. Both data sources had similar operating status accuracy. Research using spatial analysis may prefer government-maintained registry derived data due to high location information accuracy, whereas studies looking at broad trends across states may prefer webservice derived. State level COVID-19 restrictions had minimal impact on ascertainment of cannabis retailer operating status and location information via phone survey derived from webservices and government-maintained registries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".