The experience sampling method: examining its use and potential in tourist experience research
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
Though a valid and widely used approach in leisure, recreation, and psychology, the experience sampling method (ESM) is rarely used in tourism studies as a way to collect data on immediate conscious experiences during tourist events. This paper examines the use of ESM as it relates to tourist experience research. We begin by introducing ESM before exploring the application of this method to emerging smartphone technology. We then introduce a research approach, which incorporates the use of a digital ESM modified to act as a predominantly qualitative procedure, using voice recording software, to study the experience of educational tourists in Peru. The data gathered using this approach are analysed to examine the application and operational aspects of ESM. We consider the methodological implications of this research method by presenting findings on the length of qualitative discussions, reported mood, qualitative content related to ESM procedures, and post-trip recollection of ESM. The discussion that follows focuses on evidence of participant burden, reactivity, and anthropomorphism related to the use of smartphones as data collection tools. This paper concludes by outlining future research areas, with specific reference to spatial aspects, affect, and smartphone use, which expand the potential of ESM in tourist experience studies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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