Political Economy of Destination Image: Manufacturing Cuba
Bibliographic record
Abstract
The overt manipulation of tourist destination image (TDI) is a commonly accepted practice among tourism destination marketing organizations, as well as, tourism business interests. While there has been significant critique of the business of tourism and tourism marketing's role in worldmaking—as recently covered in the pages of this journal and in other publications like Tourism Geographies — less scrutiny has focused on how public policy acts, through tourism, as a salient worldmaker. This article from Canally and Carmichael (in Canada) constructs a framework incorporating models from tourist destination image research and critical theory to determine how governmental public policy, both domestic and international, influence TDI formation. This framework is then used to conduct a critical discourse analysis of the three key US policy documents that formulate the US government's stance towards diplomatic relations with Cuba. The result is a political economy of TDI, which traces the influence of intergovernmental and extra-governmental power structures that manipulate the image of a potential tourist destination (Cuba), to manufacture a discourse that aligns with the ide- ologies of the political elites in the US. A conceptual model of governmental manipulation of image formation agents is proposed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".