Bibliographic record
Abstract
A tourist typology based on an ex ante rather than an ex post approach is proposed and examined. An ‘ex ante’ tourist classification can facilitate the planning process and provide a basis for tourism marketing. Five conceptual tourist classifications were formulated. Two samples (N = 615, 1997) and (N = 528, 2002) were used to test the proposed classification. The ‘social tourist’ classification (goes where friends, family and neighbours go) captured over 45% of the tourist classifications, followed by the ‘conventional tourist’ (19.8%, relies largely on the services of a travel agent), the ‘marketing tourist’ (17.5%, goes to places that are widely advertised), the ‘planning tourist’ (10.7%, plans all aspects of the vacation trip in detail), and finally the ‘impulsive tourist’ (6.1%, decides on the spur of the moment). The respondents' classifications were also confirmed by a separate set of data (20 questions) which describe each tourist category. The validity of the theoretical tourist typology was tested by a ‘confirmatory factory analysis’ to ensure that the conceptual model and the 20 questions were compatible.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".