Cruise Ship Itineraries: an Investigation of the Effect of Itinerary on Cruise Pricing
Bibliographic record
Abstract
This study investigated cruise ship stateroom pricing to determine if cruise ship itinerary has a significant effect on stateroom pricing. The study analyzed pricing data for cruise ship voyages originating and returning to a North American port. Cruise prices were reduced to a price per day for all voyages and linear regression analysis was used to investigate if cruise ship itinerary had a significant effect on cruise ship pricing. A linear regression analysis of the data revealed that cruise ship itineraries have a significant effect on cruise ship stateroom pricing, and the regression model explained a significant proportion (31%) of the variance in cruise ship stateroom pricing. Hawaii and Alaska cruise itineraries reported the highest mean cruise price per day among the itineraries investigated. West Coast Mexico and Western Caribbean cruise itineraries reported the lowest mean cruise price per cruise day among the itineraries investigated. Northern itineraries (Alaska, Canada/New England, Bermuda) reported a higher mean cruise price per cruise day than do the cruise itineraries further south (Caribbean, Bahamas, Cuba, Mexico). To date, the effect of cruise line itinerary on cruise pricing has not been explored in academic research. This study has strong implications for better understanding of the effect of different cruise itineraries on cruise line pricing.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".