<b>Insights from Role Theory: Understanding Golf Tourism</b>
Bibliographic record
Abstract
Role theory is one of the oldest perspectives used in the social sciences to describe and explain behavior. In this paper we draw upon role theory as it has typically been articulated in sociology. We examine the two traditional approaches to role theory, namely functionalist and symbolic interactionist perspectives, and we discuss the call for an integrated role theory (e.g., Biddle, 1986 Biddle, B. 1986. Recent development in role theory. Annual Review of Sociology, 12: 67–92. [Crossref], [Web of Science ®] , [Google Scholar]; Turner, 1979 Turner, R. 1979. Strategy for developing an integrated role theory. Humboldt Journal of Social Relations, 7(1): 123–139. [Google Scholar]/80). We suggest that role theory and its applications in tourism studies might provide a framework to aid in the classification and explanation of different types of sport tourists. To illustrate this potential application, one form of active sport tourism, the golf tourist, is examined. Using data from a Canadian Tourism Commission study (Coopers & Lybrand, 1995 Coopers and Lybrand. 1995. Domestic Tourism Market Research Study, Ottawa: The Canadian Tourism Commission. [Google Scholar]) 492 respondents who indicated that golf was an important part of their travel were analyzed. Cluster analysis and discriminant analysis revealed four distinct types of golf tourist: the sport tourist; the discerning tourist; the resort tourist; and the reluctant tourist. We suggest these findings provide initial support for our proposition that role theory may provide some insights for sport tourism research, not only in classifying different types of sport tourist, but also in explaining preferences for certain sport tourist roles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".