Parametrizing the unmeasurable: Urban qualities as quantitative parameters for computer games
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Parametric design and gamification rely on quantitative factors that can be easily translated into computer language. However, measuring and quantifying the complex urban qualities poses a challenge. This leads to the question of how to incorporate complex spatial quality into parametric design. This research, therefore, proposes a method to parametrize and quantify urban qualities by extracting main spatial qualities from three main sources, developing a comprehensive list of qualities that can be effectively parametrized, breaking them down into more tangible parameters, and assessing their interrelations within a system model. The results reveal that although urban qualities are complex, they are better defined and parametrized when their relations and originating factors are fully investigated. Furthermore, qualities are classified according to their degree of connection to other qualities within the system model and the nature of these connections. This classification results in six categories: Main Instigator, Mediating and Consequential qualities, as well as Minimally, Moderately, and Highly connected qualities. This research contributes to urban parametric design by providing a method to parametrize urban qualities and gamification fields, allowing developers to implement city complex qualities into the games.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it