Vanishing Peripheries: Does Tourism Consume Places?
Bibliographic record
Abstract
ContextThe notion of the periphery and its relationship to tourism is one that has been a source of debate for many years. The concept of a periphery obviously raises the question of peripheral to what, while the term is often used in a relatively negative context with respect to levels of development and/or influence on central government decision-making. What the term does raise of course is the extent to which location still matters at a time when physical, virtual, capital and human mobility is supposedly greater than ever before. The four contributions therefore highlight a number of key points and debates surround the relative importance or, and relationship between, location and tourist movement.The lead piece by C. Michael Hall, from the Pacific periphery of the South Island of New Zealand, is charged with the topic of does tourism consume place and therefore lead to the loss of the periphery, and perhaps some of the very qualities that attracted tourists to it in the first place. The first response by David Harrison, also from the Pacific, looks at both the geographical and broader sodal scientific understandings of periphery. The second response from David Weaver utilizes the concept of experiential consumption to interrogate Hall's paper and also link to Harrison's reference to the importance of development theory in understanding notions of periphery. The final response from GeoffW all approaches the topic from an overtly geographical perspective and brings the research probe full circle.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".