A critical framework for interrogating the United Nations Sustainable Development Goals 2030 Agenda in tourism
Bibliographic record
Abstract
Research in the area of sustainable tourism continues to grow, however a lack of understanding regarding necessary action inhibits progress. McCloskey’s (Citation2015) critique regarding the failure of the MDGs, as a direct result of a lack of critical consciousness, and understanding of the structural contexts of poverty and under-development, provided the impetus for our work. McCloskey (Citation2015) signals the important role of education in fostering transitions to sustainability. As such, we have applied our critical lens to the 2030 United Nations Sustainable Development Goals. Our paper offers tools for critically thinking through the potential for the SDGs to help shape the tourism industry for more sustainable, equitable, and just futures. We positioned six themes to serve as a conceptual framework for interrogating the SDG agenda in tourism; arising from our considerations of both reformist and radical pathways to sustainable transitions in tourism: critical tourism scholarship, gender in the sustainable development agenda, engaging with Indigenous perspectives and other paradigms, degrowth and the circular economy, governance and planning, and ethical consumption. We address these core themes as essential platforms to critique the SDGs in the context of sustainable tourism development, and highlight the cutting edge research carried out by our contributors in this special issue.
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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 | 0.029 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.015 | 0.089 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".