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Record W4283372508 · doi:10.1177/13567667221109269

Holiday photographic trends: Geographic origin and the male/female divide

2022· article· en· W4283372508 on OpenAlex
Anja Pabel, Leonie Cassidy

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal Of Vacation Marketing · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsTourismSocial mediaAdvertisingPreferenceGeographyMarketingBusinessPolitical science

Abstract

fetched live from OpenAlex

This study aims to examine the influence of geographic origin and gender on photo-taking and photo-sharing behaviours. An online survey was circulated in four geographical areas: Australia, Canada, India, and Malaysia. The survey questions asked respondents’ preferred types of photographic device, photo-content, photo-taking motivation, and photo-sharing behaviours while on holiday. Data were analysed using crosstabulation with Chi-square, controlling for gender, and reporting strength of association with Cramer's V. Results show geographic origin and gender are significant indicators of tourists’ photo-taking and photo-sharing behaviour. The number one preference for male/female respondents from India and Malaysia is taking photos of family, which are shared on social media. While nearly a third of male respondents from Australia and Canada do not share holiday photos on social media. Knowledge of this type may assist tourism marketers and destination marketing organisations (DMOs) to personalise their tourism offerings according to geographic region and gender.

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.

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)0.018
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.319
Teacher spread0.295 · 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