Performativities, pandemic pressures and ‘patchwork’ tourism places: Editor-in-Chief notes
Bibliographic record
Abstract
We open 2024 with four articles that resonate with Tourist Studies' goals in convergent but also divergent ways.They consider a range of themes from transformations in tour guide roles, refugee camp-based volunteer tourism, technology-enabled film tour experiences to social media shaped tourism stagings and practices.While taking place in diverse geographical regions and political-social situations from lockdown China to pre-viral Scotland, these four articles converge on at least two significant research trajectories.Two of these articles (Schiavone and Brandellero, 2024;Tham et al., 2024) are united in their examinations of film and social mediated tourism place practices while the remaining two (Di Matteo and Daminelli, 2024;Ren et al., 2024) converge on critical interrogations of worker-volunteer agencies and struggles.The notion of a 'patchwork' had also been employed, in two divergent localities in most of our tourism imaginations -Schiavone and Brandellero (2024) taking on the multi-mediated and multi-layered aspects of experiences to discuss Edinburgh's app-based film tourism while Di Matteo and Daminelli (2024) conceptualised their fieldwork at Lesvos' refugee camp as a 'comparative patchwork autoethnography'.We trace these convergences and diversity here and acknowledge their connections with existing corpora of work in the journal.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".