Les Produits Forestiers non Ligneux: Une Opportunité de Développement Touristique Régional
Bibliographic record
Abstract
Des experiences en cours a travers le monde montrent que les activites recreotouristiques basees sur la decouverte des produits forestiers non ligneux (PFNL) sont une strategie efficace pour diversifier l’economie en milieu rural. Toutefois, au Quebec, les entreprises touristiques basees sur la mise en valeur des PFNL sont peu nombreuses, tout comme les etudes qui traitent de ce sujet. Evaluer les benefices que pourrait en tirer une collectivite avant d’investir dans le developpement de cette filiere devient donc pertinent. Un sondage realise aupres des clients des Entreprises Essipit a montre que les touristes etaient interesses a participer a des activites portant sur les PFNL, principalement lorsqu’elles sont guidees. Notre etude a aussi permis de conclure que la Premiere Nation des Innus Essipit pourrait beneficier de la mise en valeur de sa culture pour se distinguer aupres des touristes et pourrait, par le fait meme, faire revivre au sein de sa communaute cette activite traditionnelle qu’est la cueillette en foret. La clientele touristique a Essipit etant suffisante et suffisamment interessee, la mise sur pied de telles activites pourrait apporter des retombees economiques et sociales pour la communaute. Mots-cles: PFNL, recreotourisme, retombees socio-economiques, activite traditionnelle, Premiere Nation des Innus Essipit
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".