From Sustainable Development Goals to Sustainable Cities: A Social Media Analysis for Policy-Making Decision
Bibliographic record
Abstract
The United Nations (UN) adopted the seventeen “Sustainable Development Goals” (SDGs) in early September 2015. One of these goals is SDG 11, which refers to the sustainable cities and communities. In this context, local governments face the challenge of aligning with this objective. As a result, they are increasing outreach to their organizational boundaries to involve citizens in policy making and strategy development, continually listening to citizens’ voices. One of the methods citizens use to express themselves is social media. This paper will emphasize social media platforms and specially Twitter to explore the public discourse about cities in the context of SDG 11. We applied descriptive quantitative and qualitative analysis to analyze the tweets that include terms and hashtags referring to the SDG 11. The data analysis process is composed of three major procedures: 1-Engagement analysis, 2-Trends based analysis and 3-Data Insights. Our results show that: 1-the COVID’19 pandemic negatively impacted users engagement towards SDG 11, 2-new technologies such AI and IoT are gaining more importance to help cities reach SDG 11, and 3-the SDGs are related and one SDG can impact other SDGs.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".