Space in transformation: Public versus private climate change adaptation in peripheral coastal tourism areas—Case studies from Quebec, Canada
Bibliographic record
Abstract
Abstract Climate change makes the tourism industry vulnerable, as many of its resources will be heavily impacted by its effects. Coastal destinations are likely to be the most affected by rising sea levels and extreme weather events, calling for a sociospatial analysis of the dynamics of peripheral coastal tourism communities. Using a production of space framework, we describe how tourism space is produced and (re)produced in two Canadian communities located along the St. Lawrence River estuary: Tadoussac and Notre‐Dame‐du‐Portage. A case study methodology including observation, semistructured interviews, and discourses analysis is applied to deconstruct the sociospatial process of climate change adaptation. The main findings stress the importance of discourse and land tenure strategies used by different stakeholders. Managers of publicly owned land tend to make environmental strategies (green infrastructure) central to their adaptation strategies, whereas private land owners tend to use man‐made interventions (grey infrastructure) and closing space strategies to protect and enhance their land values in response to the increasing threat and evidence of climate change impacts. The results call for further research that takes the social processes of value creation embedded in land tenure and land markets into account.
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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 | 0.004 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".