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FROM NORTH TO SOUTH: THE ENVIRONMENTAL BENEFITS OF NORTH AMERICAN RESORTS

2025· article· en· W4413192285 on OpenAlexaboutno aff
Olena Levytska

Bibliographic record

VenueCurrent problems of architecture and urban planning · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyEnvironmental resource managementEnvironmental planningEnvironmental ethicsEnvironmental protectionEnvironmental science

Abstract

fetched live from OpenAlex

The article promotes hotels and resorts that offer eco-initiatives. The experience of eco-oriented institutions in North America is considered, in particular, certification of construction according to international standards, safe materials in the interior and exterior, the use of secondary raw materials, energy saving, and leisure associated with interaction with nature is evaluated. The aim of the work is to determine typical eco-initiatives for resorts under consideration in the region, as well as to evaluate these initiatives and the overall impression of guests' stay in hotels and resorts. In particular, service, nature, leisure, rooms and exterior were evaluated. The frequency of positive customer reviews was calculated according to the specified criteria. It is determined that service has become an important and frequently mentioned criterion. An important criterion in choosing a resort is proximity to nature, for example, to a lake, forest, mountains. Having visited such places, guests often mention outdoor activities in reviews, for example, skiing, canoeing, cycling. It does not matter whether the hotel offers such entertainment, or whether the service for the provision of such services is located near to the resorts. When evaluating rooms, a large space, the presence of sound insulation, a comfortable bed and cleanliness are often distinguished. Exterior features are rarely mentioned. The paper carried out a comparative assessment of resorts in Canada, the USA and Mexico. Their features and advantages for tourists are indicated.

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.

How this classification was reachedexpand

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 score0.097
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.286
Teacher spread0.265 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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