Mediating cultural encounters at sea: dining in the modern cruise industry
Bibliographic record
Abstract
Before the mid-twentieth century, cruises were largely the preserve of elites. However by 1970 there was a dramatic shift toward a predominantly middle-class customer base; this change generated a need to revamp menus to satisfy the tastes of a new type of client. The mass-market cruise lines that dominate the modern era of cruising – from 1970 – increasingly offered passengers cuisine marketed as exotic – in ways that evoked ethnic or geographic ‘Others’. Companies used food as a way of mediating encounters between passengers and foreign cultures. Marketing plays a key role in determining the place of a dish in the familiar/exotic binary. In mediating cultural encounters, cruise lines demonstrate how they want passengers to conceptualise racial, social, and cultural Others. Today, cruise ships contain ethnically themed foods, spaces of consumption, and culinary service. Cruise lines offer these immersive ethnic themes to tourists on platforms that are constantly mobile, resulting in a fundamentally unique business model. In performing this combination, companies encourage tourists to immerse themselves in as many different cultures as possible, though in expedited ways that are inherently and intensely mediated.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".