From Djerba to Glasgow: have declarations on tourism and climate change brought us any closer to meaningful climate action?
Bibliographic record
Abstract
The United Nations has declared climate change a code-red for humanity and the 2020s the decisive decade to avoid dangerous climate disruption. The 26th Conference of the Parties in Glasgow, Scotland represents a milestone event and potentially the last chance to keep the Paris Climate Agreement 1.5 °C policy goal within reach. The tourism sector has responded to this critical moment by releasing the Glasgow Declaration: A Commitment to a Decade of Tourism Climate Action. As the third such declaration over 20 years, this paper asks whether it brings the sector closer to an action agenda commensurate with the climate emergency the sector has declared. While the Glasgow Declaration includes some positive advances, we find few themes and recommended actions that were not introduced in previous declarations over a decade ago and inaction on several past recommendations. There is no evidence that the declarations have altered the growth trajectory of sector emissions or influenced the integration of climate change into tourism policy and planning. The climate crisis demands a sectoral response no less than that to the Covid-19 pandemic, and we find the Glasgow Declaration ill-equipped to stimulate the systemic change required by the net-zero transition and accelerating changes in climate.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".