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Record W2269962415 · doi:10.1080/14724049.2015.1118108

Conceptualising host learning in community-based ecotourism homestays

2016· article· en· W2269962415 on OpenAlexafffund
Kapil Dev Regmi, Pierre Walter

Bibliographic record

VenueJournal of Ecotourism · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEcotourismTourismNegotiationAgency (philosophy)SociologyCommodificationCurriculumStructure and agencyPedagogyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This paper draws on practice-based theorising in workplace education to conceptualise the learning of hosts in homestay provision in community-based ecotourism (CBET). The paper first discusses the spatial and social dimensions of community homestays, reviews literature on homestay tourism, cultural commodification and colonialisation of local communities, and argues that agency is due homestay hosts in negotiating CBET on their own terms. Billet's model of workplace learning – describing curriculum practices, pedagogic practices and epistemological practices – is then used to conceptualise host learning in CBET, drawing on a comprehensive review of published research on homestays in CBET. The paper argues that host learning and education give hosts the capacity for local self-determination and control of ecotourism development and management. The conclusion offers directions for further research and practice.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.019
Scholarly communication0.0060.005
Open science0.0010.008
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.297
Teacher spread0.268 · 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

Citations58
Published2016
Admission routes2
Has abstractyes

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