Emerging from the shadows of the COVID-19 pandemic and building back better: <i>Tourist Studies</i> and Asian critical tourism scholarship
Bibliographic record
Abstract
Four papers on tourism in Asia constitute this issue.Thus, the first issue of 2023 offers our readers a platform to reflect upon Asian critical tourism scholarship and the journal's support of this growing area within tourist studies. A call for Asian tourism scholarship in post-COVID-19 or still-viral timesPrior to the COVID-19 pandemic, tourism economies in Asia were booming in ways unimagined previously.References to 'The Asian Century', a term invented by former Chinese leader Deng Xiaoping to proclaim the foreseeable economic triumph of the continent, signalled the traction gained by tourism research on Asia in terms of the quantity of articles published in international as well as domestic platforms (Sin et al., 2021).'Asia' is a tricky term.The cultural and economic diversity of Asia and its imprecise and contested borders make definitions a daunting task, yet shared challenges and opportunities do unite Asia as a constellation of peoples and places.While the coronavirus disease in 2020 put a stop both to the momentum of Asian tourism and tourism research underway at the time, in 2023 at Tourist Studies we now see possibilities emerging from the painful shadows of the pandemic -possibilities to build back the scholarship theoretically, methodologically and empirically.Tourism scholarship in Asia started off on the backs of Anglo-western concepts and methodologies (Winter, 2009).On top of cultural and societal mismatches between the 1157932T OU0010.1177/14687976231157932TouristStudiesOng and Frohlick editorial2023
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".