“Gazing” and “performing”: Travel photography and online self-presentation
Bibliographic record
Abstract
This article illustrates the self-presentations young people foreground when they visually communicate international volunteer experiences to social media audiences. Through a “categorical-content” analysis of repeated semi-structured interviews and photographic content posted to Facebook, and with theoretical support from Urry’s “tourist gaze” and Goffman’s “presentation of self,” I describe three impressions “given” and “given off” within participants’ profiles. The findings reveal some familiar touristic scenes (necessitating tribute to the well-established “family” and “romantic” gazes) and also inspire a new gazing form (incorporating “gutsy” bodily experiences). However, these holiday-like portrayals were selectively disclosed and complicated by the sentiments participants expressed during face-to-face interviews. As different self-presentations were idealized in different settings, this article helps to elucidate the situational role of the audience and offers unique analytical insights that may not have emerged had I utilized one method in isolation. Its contribution is located within its intersections: blending gazing and performing frameworks, employing verbal and visual approaches, leading to etic and emic understandings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".