Infrastructuring Public Consultation in Town Planning— How Town Planners Translate Public Consultation into a Socio-Technical Support System
Bibliographic record
Abstract
Abstract For public consultation in town planning, town planners can employ various software systems to improve the dialogue with citizens. This article looks at attempts to do so by following the work of a team of municipal town planners across four stages of public consultation held between 2012 and 2015. The study is based on detailed semi-structured interviews, field notes from regular visits to the planners’ office, and a database of public consultation comments and attendance at consultation events across the stages. Using an approach that considers planners’ work in the selection and implementation of software within institutional objectives and constraints as “infrastructure” work, we examine the joint deployment, use and effects of nine software tools and arising practices for public consultations. Our findings demonstrate how the infrastructure work of planners involved numerous interpretations about the possibilities for software adaptation and the effects of software use, which were enabled and constrained by consultation and planning requirements. The results also indicate a role for researchers in helping planners mediate between formal processes and public concerns, and illustrates how this technological-institutional struggle in infrastructuring work forms an essential part of town planners’ practice.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".