Infrastructuring Place: A Case Study of Citizen-Led Placemaking Practices in Two Urban Gardening Projects
Bibliographic record
Abstract
A proliferation of citizen-led placemaking practices, characterised by peer-to-peer production, collective intelligence and participatory design, has challenged expert-led development practices, and encouraged alternative forms of urban governance and land use.From large-scale projects to temporary urban interventions, citizens are exploring new ways of working together to reshape their communities and make place.This study builds on emerging concepts in participatory design by answering the question, How do two urban gardening groups make place?Through sketch mapping, focus group interviews and document analysis, this study combines two research traditions in an interdisciplinary analysis of the material practices and social impact of commons-based approaches to placemaking.It emphasises the important role that citizens play in creating, designing and maintaining the commons, and demonstrates that individuals working outside a professional urban design context can (and do) innovate to create meaningful community places, by advancing open and decentralised forms of participation, production and knowledge.Commuting from Montreal to Ottawa would have been much more challenging without the help of friends.I would like to thank Corinna Robitaille and Linda Pelude, and Amanda Quance and Nick Ackerley for providing me with a comfortable and welcoming place to stay during my weekly commute to Carleton University.I would also like to express my gratitude to my mother-in-law Beth Lehrer and father-law Marty
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".