Bibliographic record
Abstract
Protected areas are tourist destinations where, contrary to popular imaginaries, communities live. In and around those territories, actors implement solutions that meet the needs of their community (Soubirou & Jacob, 2019); they demonstrate social innovation. In doing so, they contribute to new compromises and new forms of regulation or governance (Klein et al., 2014). Sometimes, out of attachment to the territory, they choose alternative paths (Crosetti & Joye, 2021) such as NOvation (Godin & Vinck, 2017). The objective of this study is to analyze how mountain touristic territories articulated around protected areas generate innovation in order to face the challenges they encounter. In the form of a multiple case study, three territories are studied: Mont-Orford (Canada), Banff (Canada) and Aspen (United States). Contemporary issues are discussed in the continuity of their historical roots (see Crosetti & Joye, 2021). The results highlight the specificity of mountain tourism territories where protected areas are found, and the resulting double valuation they are subjected to (by tourism and conservation), that sometimes constrain but also foster (social) in-NOvation (in-NOvation [sociale] in French), a term introduced to name a broadened conception of innovation. It manifests itself in unsuspected spheres: the past, nature, within government institutions, through governance and dynamics of the territories. Touristic and protected mountain territories are not “on the fringes” of innovation, rather, their characteristics (rugged relief, relative eccentricity, exceptional character) make them the breeding ground for a distinction between (social) in-NOvation and the leitmotif of innovation “at any cost” (Everett Rogers, 1963 in Godin & Vinck, 2017). Considering recurring or acute issues, this study contributes to the scientific study of innovation, which is imbued with the prevailing pro-innovation bias (Boutroy et al., 2015; Godin & Vinck, 2017).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".