Evolutionary economic geography: reflections from a sustainable tourism perspective
Bibliographic record
Abstract
Evolutionary economic geography (EEG) is receiving increasing attention from tourism geographers with over 30 publications explicitly incorporating EEG into tourism between 2011 and 2016. Many of these contributions are conceptual, which is not surprising given the novelty of EEG within economic geography, in general, and tourism, in particular. However, a sizeable number of these are built on detailed case studies, using EEG as an analytical lens rather than as a conceptual point of departure. Thus, many tourism researchers have found that EEG has great potential for understanding change in tourism destinations. In this Research Frontiers paper I critically reflect on this early research of EEG in tourism geographies from a sustainable development perspective. In the cases presented, EEG offers a fresh understanding of two related challenges in each of two separate aspects of sustainable tourism development. First, pro-growth governance models can be disrupted by engaged local stakeholders in order to make tangible sustainability gains but these gains remain precarious over time as pro-growth governance models prove tenacious in the very long-term. Second, regional institutional legacies hamper new path emergence in two ways – through institutional inertia which keeps the region&s;s focus on past success in other sectors and through the (possibly competing) institutional imperatives of the dominant and emerging tourism sub-sectors or sub-regions. These challenges are illustrated through two complementary Canadian cases drawn from the extant literature – the mass tourism destination of Niagara and the resort community of Whistler. I highlight how a sustainable tourism perspective can also help to critique EEG theory and empirics in line with other recent political economy critiques in economic geography. I conclude that sustainable tourism, at its best, is an established reflexive lens which will help to develop, validate, and challenge aspects of EEG theory within tourism studies, in particular, and economic geography, in general.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".