LANDSCAPE ENTREPRENEURSHIP: LESSONS FROM THE MONT SAINT HILAIRE NATURE CENTRE
Bibliographic record
Abstract
AbstractThe following sections are included:IntroductionEntrepreneurship as a mean to meet social needsFrom Classic to Social EntrepreneurshipCase Study: The Mont Saint Hilaire Nature CentreThe Nature CenterThe history of the Mont Saint Hilaire Nature CentreThe early years (1958–1972)The conservation phase (1972–1995/6)A mission statement for a dual undertakingThe beginning of a new eraThe Nature Centre: Exerting influence on regional ecological issuesGetting organized with new tools: development plan and multimedia toolsNature centre partnerships with regional authoritiesThe Nature centre's partnering approach: making partnerships workThe Nature Centre as Landscape Entrepreneur: Four PerspectivesLandscape entrepreneurship as a component of social entrepreneurship and organizingLandscape entrepreneurship and the challenges of stakeholder engagement in the processShared values and intentions as the basis for the entrepreneurial dynamic of partnershipsLandscape entrepreneurship and the construction of resilience in social-ecological systemsBuilding a buffer: shifting the focus from the mountain to the landscapeProviding direction for self-organization: mobilizing local supportBuilding a center for learningLandscape entrepreneurship as seen through the experience of implementing a UN framework for conservationFramework for biosphere reserve activities: the limitations of the Seville StrategyInnovative consultationLearning from entrepreneurial activities at the Mont St-Hilaire biosphere reserveLessons for Landscape EntrepreneurshipLandscape entrepreneurial systemConclusionTensions in landscape entrepreneurshipReferencesWebsites
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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 | 0.002 | 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 | 0.015 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".