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Record W1991979716 · doi:10.1108/17506181111174628

Tourist photographs: signs of self

2011· article· en· W1991979716 on OpenAlexaff
Russell W. Belk, Joyce Hsiu‐yen Yeh

Bibliographic record

VenueInternational Journal of Culture Tourism and Hospitality Research · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsYork University
Fundersnot available
KeywordsTourismOriginalityTRIPS architecturePhotographyTyingIdentity (music)Value (mathematics)AdvertisingVisual artsSociologyHistoryComputer scienceAestheticsQualitative researchBusinessArtSocial science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to investigate the reasons that tourists capture images of their trips on cameras or camcorders. Design/methodology/approach Over a period of approximately five years, the authors observed, photographed and interviewed tourists taking photos or videos in diverse international locations. Upon returning home, informants e‐mailed their trip photos together with descriptions of what the images meant and what they had done with them when at home. These data were archived and interpreted in line with the central research questions. Findings Why does almost every tourist carry a camera or camcorder? What are they doing making these images? And what do they do with them once they return home? The accompanying video conveys most of the findings, while the manuscript elaborates on certain theoretical points and provides contextualizing and supportive evidence from the literatures dealing with tourism and photography. Originality/value The paper suggests that the images form part of an identity project, serving as a means of conveying internal tales to the self rather than as a means of, beyond the immediate family, communicating with others. The images act as tools for displacing meanings that are too fragile and tenuous to be contained in the fragile present as Grant McCracken describes more generally with regard to tying hopes and dreams to places and times of the past and future.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.277
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.081
GPT teacher head0.405
Teacher spread0.324 · 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.

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

Citations83
Published2011
Admission routes1
Has abstractyes

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