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Record W2782045312 · doi:10.25904/1912/2729

Risk-taking on Her Lonely Planet: Exploring the Risk Experiences of Asian Solo Female Travellers

2017· dissertation· en· W2782045312 on OpenAlexfundno aff

Bibliographic record

VenueGriffith Research Online (Griffith University, Queensland, Australia) · 2017
Typedissertation
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
FundersGriffith Institute for Tourism, Griffith UniversityRyerson University
KeywordsPopularityTourismNegotiationEmpowermentRisk perceptionFemininityIndependence (probability theory)GeographyChinaGender studiesPerceptionPsychologySocial psychologySociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Recent advances in gender equality have improved women’s employment and with their increased economic independence, women now have greater opportunities to travel and more choices to make about travel. A rising interest in solo female travel, which has been regarded as a means of demonstrating women’s empowerment, is observed in many parts of the world, including Asia. Nevertheless, this form of independent travel may expose women to risk when travelling alone in the gendered and sexualised tourism space. Although risk appears to be a prominent feature of women’s solo travel experience, little research in this area has considered risk as an independent subject of investigation. In fact, there are only a handful of studies on solo female travel despite its rising popularity and no one has studied the experiences of Asian women. In response to these gaps, this thesis explores the risk perceptions and negotiation strategies of Asian women who have travelled alone and the implications of risk in these women’s lives and in relation to the social world.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0010.003
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 head0.179
GPT teacher head0.393
Teacher spread0.215 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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