Bibliographic record
Abstract
The commodification of tourism includes the production and dissemination of words and images used to sell experiences. Tourism marketers use highly visible assurances that the experiences available are pleasurable, safe and convenient. These assurances are projected into a promotion-oriented front region. However, fine print terms and conditions – the rules that define the rights and obligations of tourism providers and consumers – occupy a back region that, in part, runs counter to the impressions fostered in the front region. Tourism marketing, it is argued, involves two mutually supportive domains that drive the sale of tourism: conspicuous (front region) words and images as well as inconspicuous (back region) fine print. A more comprehensive conceptualization of tourism marketing should consider the functions performed by the fine print. The four Ps typically associated with the marketing mix – product, price, promotion and place – are used for the purpose of organizing the analysis of the complexly crafted small type that is often hidden in plain sight but sometimes receives highly publicized scrutiny within a media-generated front region. Fine print terms and conditions (whether successfully obfuscated or the subject of a media expose) are responsible for creating a ‘stipulation surplus’, money earned or saved via the imposition of restrictions. The marketing of tourism involves the making of front region promises tempered by the imposition of back region parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.020 | 0.009 |
| Insufficient payload (model declined to judge) | 0.336 | 0.254 |
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".