Postsecondary organizations and their role in advancing sustainable smart cities: towards a system-oriented perspective
Bibliographic record
Abstract
Postsecondary institutions such as public and private universities have a key role to play in the development of sustainable smart cities. This paper discusses aspects of this role in terms of historical contributions, examples of contributions from the standpoint of two universities, and potential future contributions. The treatment of these aspects from a system-oriented perspective is also addressed. Researchers working on leading edge technologies have resources that enable them to introduce disruptive solutions that enhance the well-being of society. On the other hand, it is clear that different university realities demand unique actions depending on whether they reside in developing or developed countries, although common social problems have also been identified. Overall, there is an opportunity for universities to test new ideas and implement them in communities, especially where they reside. We discuss the role of universities in a broad sense, where contributions are briefly described and acknowledged. The focus is on applications for sustainability and social good that have been or could be developed in universities as new research opportunities to improve the quality of life of the general population. We also argue that it is essential to consider university contributions to the creation of smart cities in the context of a system-oriented perspective.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".