Using Visitor-Employed Photography to Investigate Destination Image
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.
Bibliographic record
Abstract
Given the dominant use of visuals in destination image promotion and the call for more pluralistic approaches in tourism analysis, the purpose of this research note is to illustrate the utility of visitor-employed photography (VEP) to elicit tourist destination image. An image study conducted at a heritage site provides an example of VEP applied in this context. Challenges associated with using VEP mainly were logistical (for visitors) and resource based (for researchers). Benefits to using this method for image assessment were high response rate (95%), unprompted visitor-generated themes and visuals, and enjoyment expressed by respondents. The VEP method provided highly visual records of what best captured the visitors’ images of the site, which then can be compared to pictures used in current promotional efforts. Results provide initial support of the usefulness of VEP to generate images of a tourist attraction and to facilitate meaningful practical and theoretical integration of visitor-determined images with destination-determined images.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it