An Individual-Based Approach to Modeling Tourism Dynamics
Bibliographic record
Abstract
To better understand the dynamics of tourism, emphasis in modeling is evolving from descriptive towards analytic, process-based approaches. We present a conceptual framework of tourism as a set of individual-based interactions between tourists and destinations occurring on a spatial, scaled landscape. We use agent-based modeling (ABM), a type of computer simulation, to operationalize this individual-based framework of tourism development and change, set in the Canadian province of Nova Scotia. The model is used to generate a series of scenarios about the impact of visitation to rural destinations through modifying individual awareness and tourist mobility variables. The findings generated with this ABM demonstrate that the spatial location of a destination in relation to a network of other destinations has implications for how that destination can capitalize on changes to tourist destination awareness and mobility. The impact of spatial location is only apparent as a result of modeling the individual interactions of tourists and destinations. This research proposes that an individual-based approach can be used to better understand the spatial, multiscaled processes and dynamics that generate emergent patterns of impact.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".