Using Correspondence Analysis to Explore the Relationship Between Information Sources and Elderly Tourist Segments
Bibliographic record
Abstract
This research aimed 1) to segment the target market of New-Age Elderly tourists, and 2) to define the data source that each group of New-Age Elderly tourists used for their trip planning by collecting data from 420 samples of Thai tourists aged between 60-80 years old in tourist attractions in cities. It was found from the findings that we can group New-Age Elderly tourists into five segments. Having analyzed the relationship between sources of tourism information and five segments of New-Age Elderly tourists the results can be summarized as follows. The first segment (‘Worried’) stressed making decisions and only felt confident after reading and having seen pictures. This group then stressed using tourism sources from leaflets or brochures. The second segment (‘Accepting aging’), saw it as a fact of life that no one can control aging and that it should be accepted. They stressed using friends and relatives as sources of tourism information. The third segment (‘Firmly with changes’) dared to face up to new things and considered life as an adventure. Plus, they sought out new and unseen tourism destinations that seemed exciting and wild so, they stressed using tourism data sources from tourism magazines and TV programs. The fourth segment (‘Young at heart’) were those with life satisfaction, who loved having fun and always felt young. They wanted to travel and stressed using data sources directly from tour agencies. The fifth segment (‘Consciousness’) were conscious in their actions and did not dwell on the past. This segment stressed using information sources from articles in travel magazines, newspapers, or journals. This study provides more understanding about the concept of the New-Age Elderly that makes a contribution to both research in the area of the New-Age Elderly and for practitioners in the tourism industry.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".