Place-making: From a Residents’ Initiative to a Participatory Effect: The Case Study of BOMBAST
Bibliographic record
Abstract
Abstract This case study illustrates an organic bottom-up place-making process, which started with the initiative of a couple of residents who created an invented folktale as a gift to their community. Through the development of a fictional character, these residents intuitively tried to embody the place’s norms and values or, in other words, the DNA of the community. The challenge, however, was how to transform this single initiative into shared meaning-making, borne by the wider local community. Two trends, noted in place-making literature, are of significance for the case. First, the case shows that, in making the place more attractive to live, the focus should be on the intangible attributes— the cultural soul of the place. Secondly, the case illustrates, through the use of the Imagineering Design Methodology, how the wider community has been enabled to become co-producers and co-consumers in the place-making process. Moreover, by actively including disruptive voices, such as those locals who were critical in the enabling interventions, the place-making process evolved. The case, therefore, shows how these interventions led to a participatory effect. VIU logo WLCE logo Information Vancouver Island University World Leisure Centre of Excellence Main image: Stichting BOMBAST, 2020. Drawing by Clema van Bekhoven © Monique Schulte – van Alphen and Nicoline de Heus2022
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.019 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".