Characterising the landscape of mycotourism initiatives in Quebec: a comprehensive overview including a classification, cluster analysis and strategic group approach
Bibliographic record
Abstract
Even if a niche activity, mycotourism has gained in popularity internationally, with various initiativzses emerging to cater to the growing interest of tourists toward mushrooms and mushroom-related goods and services. This article presents the argument that mycotourism encompasses a diverse range of activities that have yet to be fully explored and understood by decision-makers and scholars alike. This paper addresses this research gap by providing a comprehensive understanding of the diverse mycotourism initiatives in Quebec (Canada). Through an in-depth environmental scanning process, this study presents an overview of mycotourism offerings, employing a classification, cluster analysis, and strategic grouping approach to synthesise and organise knowledge based on 57 organisations. The 18 mycotourism activities were classified into five distinct categories. The cluster analysis revealed the presence of four distinct groups of micro and very small enterprises (VSEs) in the sector, each characterised by its own unique set of traits and behaviours: curated activity providers; wide-ranging service providers; learning and development focused providers; and guided adventures providers. By leveraging providers' total number of employees and total number of activities offered, we have established 4 strategic groups demonstrating that the VSEs exhibit versatility in their activities and adapt to different scales of mycotourism offerings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".