Handwaving Participatory Planning: The “Ongoing Experiment” With Dedicated Digital Engagement Platforms
Bibliographic record
Abstract
Problem, research strategy, and findings Municipal planners and local governments are increasingly using digital platforms to support participatory decision-making. In Canada, the adoption of dedicated digital engagement platforms (DDEPs)—a new generation of web-based software purpose-built for public engagement—has become widespread. Despite their proliferation, these platforms have received limited scholarly attention, underscoring the need for critical examinations of their use in context. In this study, I investigated how DDEPs shape engagement processes through interviews with 44 professionals involved in their adoption and administration across Canadian municipalities, with lessons relevant to jurisdictions elsewhere. Guided by critical perspectives on digital platforms and urban governance, the findings reveal both perceived benefits and significant challenges. While DDEPs may improve accessibility and efficiency, they also introduce new constraints on participation. The most used platforms, combined with prevailing municipal implementation practices, may unintentionally limit the democratic ideals these technologies are marketed as embodying.Takeaway for practice This research provides a conceptual framework for analyzing the impact of platforms on participatory planning. Applying this framework to interview findings, I distill key practitioner insights and offer recommendations on aligning platform functionality with participation goals, ensuring adequate administrative capacity, critically assessing how platform design shapes engagement, and developing clear metrics to evaluate whether platforms meet their democratic objectives. Absent intentional design and ongoing care, mere adoption of digital engagement platforms can devolve into a kind of performative handwaving of participatory process.
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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 | 0.156 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.060 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.005 | 0.008 |
| 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".