Lessons from conducting trans-national Internet-mediated participatory research with hidden populations of cannabis cultivators
Bibliographic record
Abstract
Background Internet-mediated research methods are increasingly used to access hidden populations. The International Cannabis Cultivation Questionnaire (ICCQ) is an online survey designed to facilitate international comparisons into the relatively under-researched but increasingly significant phenomenon of domestic cannabis cultivation. The Global Cannabis Cultivation Research Consortium has used the ICCQ to survey over 6000 cannabis cultivators across 11 countries. In this paper, we describe and reflect upon our methodological approach, focusing on the digital and traditional recruitment methods used to access this hidden population and the challenges of working across multiple countries, cultures and languages. Methods Descriptive statistics showing eligibility and completion rates and recruitment source by country of residence. Results Over three quarters of eligible respondents who were presented with the survey were included in the final sample of n = 6528. English-speaking countries expended more effort to recruit participants than non-English-speaking countries. The most effective recruitment modes were cannabis websites/groups (33%), Facebook (14%) and news articles (11%). While respondents recruited through news articles were older, growing practice variables were strikingly similar between these main recruitment modes. Conclusion Through this process, we learnt that there are trade-offs between hosting multiple surveys in each country vs. using one integrated database. We also found that although perceived anonymity is routinely assumed to be a benefit of using digital research methodologies, there are significant limits to research participant anonymity in the current era of mass digital surveillance, especially when the target group is particularly concerned about evading law enforcement. Finally, we list a number of specific recommendations for future researchers utilising Internet-mediated approaches to researching hidden populations.
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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 | 0.361 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.025 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".