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SDGs, Environmental Policy Integration, and Tourism in Canada

2025· article· en· W4408857439 on OpenAlex

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTourism Analysis · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTourismEnvironmental policySustainable developmentBusinessEconomic geographyRegional sciencePolitical scienceEnvironmental planningGeographyArchaeology

Abstract

fetched live from OpenAlex

Environmental policy integration (EPI) is put forth as a potential tool for achieving the Sustainable Development Goals (SDGs) and transitioning to a more sustainable global community. Tourism is one sector that consists of multiple industries and has a high potential to achieve these goals. Examining local, provincial, and federal tourism policies, as well as undertaking in-depth interviews, this research sought to determine to what extent the SDGs are integrated into different levels of Canadian tourism policy and strategy using the framework of EPI to determine inclusion, consistency, weighting, resources, and reporting. Findings show that although there is a greater inclusion and consistency of elements relating to the SDGs at all levels of government, there is a general lack of specific mentions of the goals and even less weighting, consistency, and reporting. Implications of this research outline that a more concerted horizontal and vertical approach is needed across policy sectors as well as all levels of government.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.281
Teacher spread0.274 · 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