Global tourism in crisis: conceptual frameworks for research and practice
Bibliographic record
Abstract
Purpose The aims of this Editorial are twofold: (i) synthesise emergent themes from the special issue (ii) tender four theoretical frameworks toward examination of crises in tourism. Design/methodology/approach The thematic analysis of papers highlights a diversity of COVID-19 related crises contexts and research approaches. The need for robust theoretical interventions is highlighted through the four proposed conceptual frameworks. Findings Crises provides a valuable seam from which to draw new empirical and theoretical insights. Papers in this special issue address the unfolding of crises in tourism and demonstrate how its theorization demands multi and cross-disciplinary entreaties. This special issue is an invitation to examine how global crises in tourism can be more clearly appraised and theorised. The nature of crisis, and the extent to which the global tourism community can continue to adapt remains in question, as dialogues juxtapose the contradictions between tourism growth and tourism sustainability, and between building back better and returning to normal. Originality/value The appraisal of four conceptual frameworks, little used in tourism research provides markers of the theoretical rigour and novelty so often sought. Beck’s risk society reconceptualises risk and the extent to which risk is manmade. Biopolitics refers to the power over the production and reproduction of life itself, where the political stake corresponds to power over society. The political ecology of crisis denaturalises “natural” disasters and their subsequent crises. Justice complements an ethic of care and values like conative empathy to advance social justice and well-being.
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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 | 0.044 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.008 | 0.086 |
| Scholarly communication | 0.038 | 0.028 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".