(Re) production of coastal tourist areas and climate change adaptation in the periphery : a critical perspective.
Bibliographic record
Abstract
Space and place are the first resources of the tourist industry. Tourism is well known to transform the very own place it has built upon. Coastal tourism is a good example of how spaces, through the form of resources like the shore and the sea, are turn into a tourist product. However, as climate change is transforming the physical space of the coast, the tourist space will also be transformed on the way to adaptation. On one hand, it will transform how tourism, and tourist, interact in coastal space and, on the other hand, it will also transform the fragile cohabitation of many lands uses on the coastal space ( residential, commercial, industrial, leisure, tourism, etc.,). Shorelines of the St. Lawrence River are at the heart of the tourist activities in Quebec, Canada, but also are spaces that will be strongly affected by climate change. The erosion and coastal flooding will profoundly change the physical space enhanced by tourism. For coastal communities in the periphery, adapting to these changes is crucial for their development, hence the importance of understanding the process of adaptation that will be implemented. The research aims to explore how climate change, in relation to the discourse of adaptation to climate change, is altering the spatial development of the tourism industry in coastal destinations in the periphery. From a critical approach based on the concept of production of space (Lefebvre, 1974; Harvey, 1996), it will analyze the discourses related to adaptation to climate change in coastal tourist areas. The aim of the paper is to elaborate a production of space framework to analyze the transformation and (re)production of coastal tourist space within a capitalist accumulation process. It will also identify how the discourses of tourist development and climate change adaptation combine to transform space and place, especially coastal tourist space and how local communities can interact with those discourses. The paper will exemplify with cases from the St-Lawrence River estuary, in Quebec, Canada.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".