Characterising the landscape of mycotourism initiatives in Quebec: a comprehensive overview including a classification, cluster analysis and strategic group approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it