Visualization for Citizen Initiated Public Participation: A Case Study
Bibliographic record
Abstract
This paper examines the impact of a citizen initiated public participation process on preparers and presenters of digital visualizations for spatial design decision making. Visualization for public participation enables communication between professionals and laypeople to occur with far greater success than through conventional methods. Further, visualization utilizing real-time immersive technology allows for far more effective communication of the spatial impact of design proposals than conventional media offer, facilitating negotiation and interaction with space by providing the means to virtually walk around a digital model. In addition, the effectiveness of real-time immersive visualization in bridging the public-professional communication gap can empower the public, offering the opportunity to confront professionals and to force engagement in a process of public participation on the public's terms. Through discussion of a case study from the University of Toronto's Centre for Landscape Research (CLR), this paper examines the impact on the visualization process when the public are able to invert the conventional model of public participation by initiating the dialogue with professionals. This paper argues that a citizen initiated public participation process increases the necessity for a sound methodology and code of ethics of visualization for public participation. When the public are able to utilize technology to invert the conventional public-professional role, issues of validity, reliability and ethics are placed at the forefront of the discussion greatly increasing the scrutiny placed on both the technology and those preparing and presenting the visualization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.005 |
| 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".