Practical implications of serious gaming for urban redevelopment of Merwe4Haven in Rotterdam
Bibliographic record
Abstract
Is there added value in the utilisation of serious gaming outcome for practice in real life? One of the courses at Management in the Built Environment at the faculty of Architecture and the Built Environment of Delft University of Technology is the management game. Didactics of this course have been previously discussed at ERES. The focus of this paper is on the content and results of the latest edition. In 2017/2018 the course is given in the first and third quarter with urban redevelopment of the harbour area Merwe4Haven in Rotterdam and Schiedam as topic. Respectively 80 and 175 students have worked together in groups within the concept of roleplay. With groups of 9 students, this resulted in 30 strategies for the area. The purpose of this paper is twofold: firstly a comprehensive summary of those 30 strategies, and secondly an evaluation of the management game as a mean to reach such results, and the utility of these result for policy and practice. The research approach is at one hand a typical example of research by design – evaluation of the 30 parallel designed strategies and how it is accepted by stakeholders. From the educational point of view the research questions relate more to the level of complexity and scale which can be achieved within a course based on roleplay within the given limitations; the available time students can spend in 10 weeks and the entrance level of students halfway their study. The specific findings in this paper is a list of development strategies relevant for practice. Municipalities are using the results especially to enhance brainstorming during policy making and support the dialogue with their citizens. The more extreme solutions may not always be realistic, but can be instrumental in brainstorming and fostering discussion. The educational reflection reveals a trade-off between complexity and depth. Introducing a large scale with many stakeholders and optional impacts on the area and influences in many fields lead to a more abstract perception of the strategy where design concepts stay less tangible. The implication of this paper is to strengthen social and practical utilisation of education. With public presentation of the results society have immediate profit of the outcome and practitioners can apply the outcomes in the process of redevelopment, while students have a realistic experience (reflection of society and practice) to enrich their education.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".