The role of local cultural factors in the achievement of the sustainable development goals
Bibliographic record
Abstract
Abstract The sustainable development goals (SDGs) are the greatest agreement achieved among countries. However, international policies such as the SDGs usually forget to include local cultural factors that would enable their achievement. Culture and sustainability have been studied in several contexts; however, the role that local culture plays in achieving sustainability has not been fully explored. This research addresses that gap by focusing on the SDGs globally and according to countries' income, continent, and region of origin. Hypotheses are tested through regression models using Hofstede's six cultural dimensions at the country level and the countries' overall and partial SDG scores. Results highlight significant relationships between cultural dimensions and countries' SDG scores in general and for groups of countries, and between cultural predictors and SDGs. Overall, power distance and masculinity contribute negatively to sustainability, whereas individualism, uncertainty avoidance, long‐term orientation, and indulgence affect sustainability positively. However, results vary across regions and SDGs. This article contributes with recommendations for policy and decision‐makers to address local SDGs and manage the different cultural dimensions of countries toward the accomplishment of sustainability. Certainly not an easy task.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".