Experience Sharing on Social Networking Sites: A Glimpse into the Process, Benefits, and Drawbacks of Curated Experiences
Bibliographic record
Abstract
This study seeks to gain a greater understanding of why individuals choose to share the select experiences that they do on social networking sites and how the sharing of such experiences shapes their overall personal brand.Semi-structured interviews were conducted with 15 individuals who identified as being from either the Millennial or Generation Z cohorts and who had travelled outside of their home province in the last two years.These interviews were then analyzed using an interpretivist epistemological approach.Impression Management Theory, Personal Branding Theory, and Critical Visual Methodology Theory, as well as well-known constructs such as 'staged authenticity' and the framing of images were used to identify important themes and categorize processes.The findings suggest that the process of posting involves all four sites of Critical Visual Methodology Theory (the site of production, the site of the image itself, the site of circulation, and the site of audiencing) and that picture preferences on social networking sites are strongly influenced by the original photography themes introduced by Kodak.It was also found that image-based platforms such as Instagram, function as a digital passport where individuals use the photos that they share on Instagram from different marker locations as a virtual travel stamp providing photographic evidence that they have been to a particular location.The concept of tourism 'catfishing', that is, tourist destinations that lure people into visiting through the means of over exaggerating their appeal online, is explored as well as the negative impacts that it has on a destination.Professional benefits to posting tourism related experiences included job offers, improved portfolios for applications in the marketing field, and the ability for individuals to further build their professional networks, while personal benefits included reconnecting with friends while travelling, meeting new friends online from vii Limitations and Future Research ....
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".