Designing Glamping: Ad hoc strategies in construction, material reuse, and user experience
Bibliographic record
Abstract
Research Problem: Shifting travel habits have generated demand for novel accommodation that blends comfort with authentic experience. Glamping (“ehl-i keyf” camping in Turkish) has emerged as a hybrid model of tourism, yet its relationship to design strategies such as ad hoc remains underexplored. Aim: This study examines glamping structures as a new building model in tourism and explores their alignment with the ad hoc design strategy, focusing on improvisation, material reuse, and rapid construction. Method: A case study was conducted at Whispering Springs Wilderness Retreat (Ontario, Canada). Data were collected through field observation and a semi-structured, face-to-face interview with the owners, guided by open-ended questions on site selection, construction, materials, and user experience. Findings: Results reveal that glamping structures reflect ad hoc design in four key respects: (i) improvised techniques adapted to site conditions; (ii) extensive use of reclaimed and second-hand materials; (iii) short construction periods allowing rapid deployment; and (iv) small-scale interior improvisations that enhance authenticity and comfort. Conclusion: Glamping offers a viable design model that merges ad hoc pragmatism with contemporary tourism expectations. Beyond documenting a single case, this study suggests that ad hoc design strategies can support scalable tourism development that is both sustainable and experience-oriented, including potential applications in Turkey under the cultural lens of ehl-i keyf.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".