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Record W2560626910 · doi:10.7784/rbtur.v11i1.1221

Monasteries and tourism: interpreting sacred landscape through gastronomy

2016· article· en· W2560626910 on OpenAlexaff

Bibliographic record

VenueRevista Brasileira de Pesquisa em Turismo · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsBibliographical Society of Canada
Fundersnot available
KeywordsGastronomyVisitor patternTourismSpace (punctuation)Cultural heritageIntangible cultural heritageCultural landscape

Abstract

fetched live from OpenAlex

This article analyses the role of monasteries as a sacred space and how their relationship with tourism depicts a landscape of 'good taste'. Monasteries are examples of both tangible and intangible heritage, and are highly symbolic built spaces that have often become the guardians of tradition. They are strongly embedded within a local cultural landscape, which has determined their historical evolution. Monasteries used to be self-sufficient communities that relied on the resources available in their local environment, e.g. they produced their own wine, which was essential for the celebration of the Eucharist; or they preserved food from their own produce. Gastronomy in monasteries can be a tool to improve tourists' visitor experience, in so far as it respects the values that these sacred spaces represent. This article explores the literature on monasteries as sacred spaces; the relationship between their tangible and intangible heritage attributes; and how monasteries and their heritage are linked to tourism. This is illustrated through examples from Spain.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.016
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.227
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2016
Admission routes1
Has abstractyes

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