Beyond destination accessibility: tourism infrastructure across mobilities, technologies and embodiments
Bibliographic record
Abstract
This introductory article to the Special Issue explores the evolving conceptualisation of tourism infrastructure through the lenses of assemblage thinking and the mobilities paradigm. Moving beyond traditional dichotomies of material versus immaterial infrastructure, it embraces a multidimensional, relational and dynamic understanding of infrastructure as an active agent in shaping tourism destinations, mobilities and socio-spatial inequalities. Drawing on interdisciplinary perspectives from critical geography and mobilities studies, the paper situates tourism infrastructure within the broader infrastructures of everyday life, emphasising its entanglement with bodies, affects, policies and power relations. The Special Issue’s contributions expand on this framework through diverse case studies across Sweden, Canada, France, Spain and Italy, highlighting how infrastructures are co-constituted through tourist, residential and labor mobilities, and how they mediate access, inclusion and transformation at multiple scales. In doing so, the Issue introduces new methodological and conceptual tools – such as “transpitality”, “sensing transit” and the infrastructural backstage – to rethink accessibility, sustainability and territoriality in tourism. It ultimately calls for an “infrastructural turn” in tourism studies that critically engages with the fluid, politicised and experiential nature of tourism mobilities infrastructures in an era marked by crises, socio-spatial inequality and planetary change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".