Cultivating cannabis agritourism: Integrating service-dominant logic in Northern California’s Emerald Triangle
Bibliographic record
Abstract
Cannabis legalization presents an unexplored context to address tour guides as strategic actors in the relationship between agriculture and tourism. In California, landmark Proposition 64 (2016), which legalized recreational cannabis, opens up opportunities for agritourism in rural areas, establishing the state as a unique hub for commercial cannabis farm tours. The purpose of this paper is to employ Service-Dominant Logic (SDL) as a framework for conceptualizing cannabis agritourism as a dynamic 'service ecosystem'. Evidence from a case study carried out in the Emerald Triangle demonstrates how the strategic value-creating activities of agritourism entities, including the tour guide, catalyze value co-creation in B2C and B2B domains. A supply-side perspective highlights the tour guide as a mechanism to resolve the persistent challenge of integrating agriculture and tourism. By providing a potential solution to facilitate the coexistence of agricultural production with customer-oriented services, tour guides also strengthen the cannabis supply chain more broadly.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".