The Scale-Adusted Latent Class Model: Application to Museum Visitation
Bibliographic record
Abstract
Preferences of tourists and visitors are varied in a number of markets, making it difficult for managers to understand how underlying segments might respond to changes in service offerings. Market segments differ in preferences for specific features, as well as how consistently they make their choices. In this article, we illustrate recent developments in choice modeling that allows for simultaneously modeling feature preferences and consistency of choice. We use the Scale-Adjusted Latent Class Model (SALCM) to better understand choices in the context of a research project conducted in collaboration with six major Australian museums involving a sample of 3,685 museum visitors. We identify three preference classes of museum-goers that explain preferences for levels of 26 museum attributes: Life Force (two thirds of visitors), Educated Thinkers, and Wealthy At-Homes. Our results indicate sensitivity to general entry prices, including preference for free entry or entry "by donation." Tours are preferred if smaller, lengthier, and conducted by paid museum staff. Not unexpectedly, the findings suggest that museums should cater for children, with some classes responding positively to providing supervised child areas. Most visitors prefer museums that are dynamic, offer new experiences, and regularly update permanent displays. However, the three classes identified have different overall experience preferences; for example, Educated Thinkers see museums as an educational opportunity, but Wealthy At-Homes prefer entertaining experiences. Incentives for return visits and cross-museum promotional offers are valued by the Life Force class, but have little effect on Educated Thinkers. The SALCM approach may be attractive to other areas of tourism analysis, especially where offerings contain many attributes and potential market segments are difficult to define and understand.
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 | 0.027 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".