Citizen Science, Community-based Monitoring and Urban Planning: Exploring Ideas to Extend Conceptual and Theoretical Implications of Public Participation
Bibliographic record
Abstract
Citizen science is a concept that advocates involving non-experts in scientific research by framing research potentially as a two-way street between researchers and civil society (Hecker et al., 2019). Citizen science has produced a number of innovative approaches to participatory research, a prolific example of which is community-based monitoring (Conrad & Hilchey, 2011). This report will explore whether citizen science approaches such as community-based monitoring can provide the beginnings of some new ideas to extend existing planning theories of public participation. Since the 1980’s, communicative planning theory has emerged as the predominant alternative to rational-comprehensive planning theory (Innes & Booher, 2015). Communicative planning theory states that planners plan in the public interest only as a result of participatory processes that address power imbalances between all stakeholders (Innes & Booher, 2016). Yet, some theorists argue that planning theory and practice fails to meaningfully address these power dynamics (Brabham, 2009). This report follows from the critique that planning expertise itself determines, a priori, what counts as credible knowledge in planning processes and thus creates an inherent power imbalance between planners and civil society (Flyvbjerg, 2002). This report proposes citizen science as one potential framework to ameliorate this problem. To explore the potential for developing such a framework, a case study of the unique planning environment on the Oak Ridges Moraine, Ontario, Canada is presented, examining linkages between citizen science scholarship and planning theory.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.009 | 0.064 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".