Participatory Approaches to Placemaking: Methodological Outlines for Public Spaces in Bergamo (Italy), for Indigenous Communities in Quebec (Canada) and Popular Places in Prerov (Czech Republic)
Bibliographic record
Abstract
The main objective of this chapter is to introduce and critically assess the methodologies employed in the three research projects that focused on making use of local knowledge about public places in the placemaking process. The case studies analysed in this chapter focus on the enhancement of public places and cultural landscapes to make them more suitable for the needs of citizens. The first case is BG Public Space, a research project conducted at the University of Bergamo (Italy). The project aimed to encourage Bergamo’s inhabitants to express their opinions with respect to the functions and characteristics of the city’s public spaces. This would foster their participation in the urban redevelopment choices made by the municipal administration. A second placemaking project is proposed by researchers at Laval University in Quebec (Canada), where sustainable tourism is a driver for the protection of the Indigenous heritage and of community places. In the third case, researchers at Palacký University Olomouc carried out participatory research in Přerov (Czech Republic). The research was done in cooperation with the municipal administration. It provided a wide range of information concerning the residents’ knowledge of various popular places in the town. In general, this chapter explores the value and potential of participatory approaches in understanding and enhancing public places and cultural landscapes. The chapter also stresses the importance of tailoring these approaches to the sui generis challenges and opportunities that exist in each research context, whether they relate to the creation of urban environments or to the protection of Indigenous heritage. Our findings indicate that despite the specific context of each case, participatory approaches and community involvement are crucial for effective placemaking. This underlines the paramount importance of fostering community engagement as well as informed decision-making processes. By doing so, we can play an active role in shaping the future of our cities and preserving a rich tapestry of cultural environments for generations to come.
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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 | 0.061 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.028 | 0.046 |
| Scholarly communication | 0.027 | 0.009 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| 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".