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Record W2981357057 · doi:10.1002/jtr.2332

Space in transformation: Public versus private climate change adaptation in peripheral coastal tourism areas—Case studies from Quebec, Canada

2019· article· en· W2981357057 on OpenAlex
Dominic Lapointe, Coralie Lebon, Alexis Guillemard

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Tourism Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversité du Québec à Montréal
FundersUniversité du Québec à Montréal
KeywordsTourismClimate changeEnvironmental resource managementDestinationsSpace (punctuation)Land useGeographyEnvironmental planningBusinessEconomicsEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.134
GPT teacher head0.409
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it