Interpreting destination image through theory building approach / Noraminin Khalid
Bibliographic record
Abstract
This qualitative study is an exploratory attempt at understanding destination image through the writings of the travel writers who have undergone the FAMiliarisation or FAM programs organised by Tourism Malaysia. The idea behind the program is for the writers to experience first hand specific destinations in Malaysia and then share their experiences through their travel writings. The main focus was the content of the articles or themes of particular magazines and their writings represented endorsed information. The data in the form of the travel articles (FAM articles) were collected with the assistance of Tourism Malaysia based on the suggested criteria established. FAM articles were collected for analyses representing writers from several countries including Australia, Canada, Brunei Darussalam, USA, United Kingdom and India to name a few. Using the qualitative method of content analysis and adapting the coding procedures of open, axial and selective codings from the grounded theory approach, this study analysed and interpreted selected travel articles written by the FAM writers. The findings are then integrated with the analysis of face-to-face interviews with international tourists and then further strengthened with literature. This study addresses the gaps between destination image theories and the depiction of such destination image by travel writers. It is postulated that writers will mould the perception of the destination and therefore enhance the understanding of destination image. Although the present depiction of destination image is very much relevant within most tourism contexts, what is perhaps not considered precisely is whether such interpretation still holds when the image is derived merely from textual data as they are interpreted by FAM writers…
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".