Creative and disruptive methodologies in tourism studies
Bibliographic record
Abstract
Disruption and creativity are the two ideas around which we challenge and contribute to dismantling white, ‘western’, neoliberal hegemonic social narratives and ideologies in qualitative tourism methodologies. In tourism studies in general, and tourism geography in particular, the last decade has witnessed an emphasis on qualitative methodological research, both in terms of the topics addressed and the types of methodological tools. In many ways, this legitimisation of qualitative work mirrors developments in other areas such as human geography, sociology and anthropology. Explorations in this Special Issue contribute critical understandings of the responsibility of tourism research to be disruptive first before it can engender progress and transformation within and outside of our field. Authors debate in more depth how tourism studies can offer multidimensional, multilogical and multiemotional, methodological approaches to tourism research. This Special Issue contributors tackle the ways in which research methodologies can be creative and disruptive to the seemingly prevalent narratives within tourism studies. To further expand tourism methodologies, authors have engaged in debates about deep reflexivity, subjectivities, and dreams; messy emotions in auto-ethnographic accounts of fieldwork; ‘motherhood capital’ accessing Inuit communities; collective memory work in tourism research and pedagogy; ethnodrama and creative non-fiction; linguistic narrative analysis, and serious gaming, amongst others.
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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 | 0.127 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.012 | 0.098 |
| Scholarly communication | 0.027 | 0.017 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".