SDG-11 and smart cities: Contradictions and overlaps between social and environmental justice research agendas
Bibliographic record
Abstract
There is an increased role Information and Communications Technology (ICT) plays in the achievement of Sustainable Development Goals (SDGs). This paper focuses specifically on SDG-11 "Make cities and human settlements inclusive, safe, resilient and sustainable" and how cities are increasingly incorporating ICT toward this goal. The public discourse on Smart Cities suggests economic, social and environmental benefits are possible through the use of Information and Communication Technology (ICT). However, the increased deployment and use of digital infrastructure and processes in the name of sustainability and optimization itself is the focus of a growing body of critical literature on Smart Cities. This mini-review collates critical literature on digital infrastructures and processes related to SDG-11 and Smart Cities to identify areas of significance for further research. Although many Smart City projects discuss sustainability benefits, the distribution of benefits and risks across different communities is rarely examined. An increased use of ICT in Smart City projects can provide environmental benefits to some communities, while shifting the burden of risks to other communities. An increased use of ICT has its own energy and resource impacts that has implications for sustainability beyond the geography of individual cities to global impacts. The lifecycle and supply chain impacts of advanced ICT projects are being identified and documented. The end user of the Smart City projects may benefit significantly from the increased use of ICTs, while the environmental costs are often borne by disparate communities. In some cases, within the same city where a Smart City project is deployed, the inequities in distribution of environmental resources and services are exacerbated by layering new ICT implementations on top of existing socio-economic inequities. Therefore, this paper combines a broad view of Smart City environmental impacts, as well as a deep examination of the intersection of social justice and environmental justice issues to create more wholistic approaches for analysis of governance of Smart City projects. A more wholistic approach for governance of Smart City projects is required that includes combined social justice and environmental justice frameworks, toward achievement of SDG-11 goals.
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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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.010 |
| 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".