Bibliographic record
Abstract
The popularity of slum tourism has been growing and is a topic of substantial discussion. Proponents suggest tours bring awareness and economic opportunity, whereas others critique their voyeuristic nature and claims of community benefits. Virtual reality head mounted displays (VR HMDs) have become relatively accessible in recent years creating a visually immersive experience of a different environment. VR technology is being used by tourism promoters as well as in education and training fields to acquaint people with foreign environments. This exploratory study draws from interviews with 16 participants who declared an interest in slum tourism. Participants discussed their experiences with, perceptions of, and motivations for visiting slum communities, and then watched a VR HMD tour of a slum in Manilla. Immediately following the video participants were asked for reactions and reflections, as well as at a subsequent meeting one to two weeks later. Findings show that the VR HMD was generally positively received, and many participants expressed a sense of trust in the representation of the community and experience because of the media’s immersive nature. Participants reported having their understandings of slum communities both reinforced and challenged, leading to more confidence that their awareness of issues in general, and slum tourism specifically, were realistic. Most participants felt more inspired, confident, and comfortable to actually take part in a tour, however some expressed concerns and described feeling less motivated to visit. Discussion includes critique of the video, as well as implications for research and practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".