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Record W3212220207

Community Perceptions Of Rural Tourism Development Based on The Gender Analysis Approach

2021· article· en· W3212220207 on OpenAlexaffvenue
Fazileh Dadvar-Khani

Bibliographic record

VenueJournal of rural and community development · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsYork University
Fundersnot available
KeywordsTourismPerceptionInequalityGender inequalityGender analysisAffect (linguistics)SocioeconomicsDescriptive statisticsGeographySociologyEconomic growthPsychologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

This quantitative and descriptive research was conducted using the gender analysis approach to examine community perception regarding tourism development. It seeks to investigate the differences between the perceptions of women and men regarding tourism effects, and to describe what generates this difference? The research took place in seven villages in the northwest region of Tehran—Capital city of Iran—and from a total of 560 households, 158 households were selected as a sample. The questionnaires were completed by 50 women and 108 men who were active in tourism. The results show although women’s involvement in tourism development increased their social connections and knowledge about their society, men are more positive about the economic effects of tourism.My research findings illustrate that men and women do not have the same perception of the impact of tourism, mainly because they have different expectations and not benefited equally. Although tourism has created new job opportunities for women, because of their concentration in the secondary labour market and due to gender differences in access to tourism advantages, tourism development did not affect reducing gender inequality in the area. A comprehensive approach and gender-sensitive rural tourism planning are needed to prevent a gender gap in rural society. Keywords: gender analysis, gender perceptions, Iran, rural tourism, tourism effects, women’s participation________________________  Perceptions communautaires du developpement du tourisme rural sur la base de l'approchede l’analyse de genre ResumeCette recherche quantitative et descriptive a ete menee en utilisant l'approche de l’analyse de genre pour examiner la perception de la communaute concernant le developpement du tourisme. Il cherche a etudier les differences entre les perceptions des femmes et des hommes concernant les effets du tourisme et a decrire ce qui genere cette difference. La recherche a eu lieu dans sept villages de la region nord-ouest de Teheran, capitale de l'Iran, et sur un total de 560 menages, 158 menages ont ete selectionnes comme echantillon. Les questionnaires ont ete remplis par 50 femmes et 108 hommes actifs dans le tourisme. Les resultats montrent que bien que la participation des femmes au developpement du tourisme ait accru leurs liens sociaux et leurs connaissances sur leur societe, les hommes sont plus positifs quant aux effets economiques du tourisme.Les resultats de mes recherches montrent que les hommes et les femmes n'ont pas la meme perception de l'impact du tourisme, principalement parce qu'ils ont des attentes differentes et n'en ont pas beneficie de la meme maniere. Bien que le tourisme ait cree de nouvelles opportunites d'emploi pour les femmes, en raison de leur concentration sur le marche du travail secondaire et en raison des differences entre les sexes dans l'acces aux avantages touristiques, le developpement du tourisme n'a pas affecte la reduction des inegalites entre les sexes dans la region. Une approche globale et une planification du tourisme rural sensible au genre sont necessaires pour eviter un ecart entre les sexes dans la societe rurale. Mots-cles: analyse de genre, perceptions de genre, Iran, tourisme rural, effets du tourisme, participation des femmes

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.044
GPT teacher head0.284
Teacher spread0.240 · 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.

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

Citations1
Published2021
Admission routes2
Has abstractyes

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