Parametrizing the unmeasurable: Urban qualities as quantitative parameters for computer games
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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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".