Bibliographic record
Abstract
The Sustainable Development Goals (SDGs) framework provides a set of 17 goals aiming to enhance global well-being by reducing poverty, enhancing health outcomes, responding to gender equality, and mobilizing social justice and peace efforts. Tourism has been centered as playing a key role in marshaling the SDGs, mainly due to its economic impact as a leading global sector. With the onset of the pandemic, it is incumbent upon scholars to take the pulse and consider the broader backdrop inhibiting SDG progress, with the intention of considering how tourism may be a vehicle for progressing the goals. As such, our analysis may serve to be useful for national and local governments, policy advisors, tourism establishments, and enterprises, as well as visitors and host communities. Specifically, we need to attend to some of the challenges inhibiting progress, scrutinize the use of language, which may lead to misinterpretation, and attend to the lack of clarity for building policies supporting decision making. Thus, the aim of this commentary is to examine some of the critiques and challenges inhibiting the realization of the goals, including a lack of awareness, and understanding of the SDGs, including what each goal entails, the division of power, the role of governance, stakeholders, and financial support required for policy and decision making, along with addressing the misconceptions surrounding the implementation of sustainable approaches. Understanding some of the critiques and challenges of the SDGs may improve our understanding of the potential role the tourism sector may play in progressing the goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.061 | 0.070 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".