From Digitalization to Capturing "Cityness": is it possible to make the essence of good cities measurable with sensors and algorithms?
Bibliographic record
Abstract
This paper seeks to uncover whether or not cityness can be made measurable and suggests a possible case study to operationalize cityness. Cityness is a value comprised of city users and the built environment. While it has repeatedly been confirmed that certain characteristics of cities have tangible benefits, it remains challenging to understand how and to what extent these traits can be nurtured by the built environment. Recently, however, the increasing digitalization of public space has brought new opportunities to operationalize physical properties and human interactions that lead to cityness. This article reviews and ethically examines a continuum of experiments using digital tools ranging from GIS to IoT systems to see to what extent they can successfully quantify previously intangible traits of city life. Finally, it introduces the case study of a sensor embedded 3D printed footbridge that was installed in De Wallen, Amsterdam Summer 2021. Ultimately, the bridge provides an opportunity to study how a singular infrastructure relates to cityness over time and in relation to naturally occurring events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".