Image formation and its contribution to tourism development in Canada's northwest territories: Past and present
Bibliographic record
Abstract
Tourism research has traditionally focused on the demand side of tourist motivation and behavioural patterns to discover why tourists visit particular destinations. This thesis explored from a supply side perspective, how image and language are used by tourism suppliers/operators to promote destinations and how such images change over time in response to consumer demand. \n \nThe study focused on the representation of natural and cultural heritage in the region of Northwest Territories Canada. It compared visual and oral components of the region's heritage, history, and early literature with contemporary travel literature. Interviews with tourism suppliers and tour operators revealed their motivation in using such components to promote their products. Literary representations of destinations motivate travellers to travel, however, the same representations evoke imagery that may not be confirmed by the actual travel experience. In order to examine tourists' experiences, the related concepts of image and authenticity were studied. Any gap between imagery and experience may impact on the traveller's sense of authenticity. It may be that a strong sense of authenticity in travel experience turns on 'perceptions of possibility' evoked by pre-travel image formation. \n \nImage is a dynamic concept and it was hoped that by comparing historical and contemporary travel literature, patterns would emerge of how such changes have affected tourism development in NWT. It was discovered that unique auras of destination image formation have developed over time through creative use of language and imagery. Tourism suppliers use such imagery to differentiate product and invoke existential desire in the mind of the potential visitor. \n \nThe diversity of tourism products today means destinations must cater to a wider array of interests, constantly repackaging and re-imaging the products they offer. This study revealed that NWT tourism imagery has moved from promoting a natural heritage base to promoting a cultural heritage base. Matching the perceptions of tourists with the perceptions of travel providers leads to more effective consumer centred marketing and confirms the important role images play in providing an authentic visitor experience.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".