Under the hood: A look at techno-centric smart city development
Bibliographic record
Abstract
Techno-centric smart and intelligent city initiatives continue to garner our attention and resources, as transforming urban areas into prosperous, livable, and sustainable settlements is a long-standing goal forlocal governments. Today, countless urban settlements across the globe have jumped onto the so-called “smart city bandwagon” to achieve this goal. Most smart city projects are either focused on transformation of the existing technical and physical infrastructures of a city— brownfield developments, or greenfield urban projects. Local governments around the globe are investing in partnerships with technology companies to help shape the future of cities. Companies such as Google and Microsoft have begun to show interest in the movement with the development of Toronto’s Sidewalks Lab and Arizona’s desert city. However, more commonly governments are investing in network infrastructure partnerships4 with companies such as CISCO to develop complex networks, sensors, and data processes. For the last several months, we have been studying the range of techno-centric smart city partnerships to uncover what works and what should local governments watch out for. Here are some of the cases we have looked at.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".