A framework to evaluate the effectiveness of web-based geo-participation tools as a public participation technique
Bibliographic record
Abstract
Web-based geo-participation tools are increasingly being used to engage local populations and stakeholders during formal participatory planning processes. These tools are utilized by planning practitioners and researchers for several reasons. Most notable are the proliferation and availability of web, mobile, and desktop technologies, and the subsequent efficiency and engagement benefits of such technology-mediated approaches (e.g. facilitating “lunch-time” participation, expanded accessibility to greater and more diverse populations). With the growing proliferation of web-based geo-participation tools in planning practice and research, it is imperative to evaluate their effectiveness as public participation techniques. The present paper proposes a framework that defines and assesses the effectiveness of using these tools to engage the public during formal planning processes relating to urban intensification. To this end, the proposed framework adapts the Analytical Hierarchy Process to prioritize and determine numeric weights/scores for a set of applicable options with respect to three distinct participation criteria. Ultimately, this framework suggests that the most effective geo-participation tools are those deployed before planning decisions are made, allow for multiple public inputs of varying magnitudes, and contain pre-defined options with open-text commenting.
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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 | 0.045 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".