An Examination of Degrowth Frameworks: Localizing, Socializing, and Regenerative Tourism
Bibliographic record
Abstract
A growth-driven market and orientation are recognized as responsible for mass tourism, overtourism, tourism resource degradation, and significantly, the acceleration of the climate catastrophe. In response to the inimical impacts generated by the tourism sector, several responses have emerged to pave a way for more intentional and responsible ways of cocreating tourism approaches that generate benefits for the destination communities where tourism takes place. Regenerative tourism, localizing tourism, and socializing tourism have intentionally been centered as responses to growing concerns in tourism; however, there is a paucity of scholarship exploring the unique attributes of each framework exploring how they may work together to advance just futures in tourism. To respond to this gap, we examined the three degrowth frameworks by adopting an Interpretive Grounded Theory methodology guided by constant comparative analysis. The aim of our analysis is to examine each framework to consider the common threads, determine what sets the frameworks apart, and importantly, consider how they may fit together. Our analysis reflects on how pulling on the strengths of each of the frameworks may provide the much-needed guidance for tourism stakeholders interested in supporting a more inclusive and impactful tourism sector in consideration of just futures.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.013 | 0.062 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".