AN EXAMPLE OF DIGITAL ENGAGEMENT PLATFORM FOR LARGE SCALE COMMUNITY ENGAGEMENT USING AURALIZATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The global COVID-19 pandemic has resulted in social distance restrictions that have limited the ability for transport authorities to undertake in-person community engagement activities and consult on proposed local infrastructure developments.Potential increases in noise levels or change in acoustic environment can often be a key concern for residents living close to a proposed development.This paper documents the approach taken to engage with local stakeholders regarding a proposed new light rail metro line in Toronto, Ontario, Canada, using an innovative online web-based auralization tool.The tool allows the existing trains and planned new metro trains to be compared interactively in an environmental context and with and without acoustic mitigation interventions.The paper discusses the benefits, challenges and limitations associated with the delivery method and provides an overview of the auralization approach of the proposed new metro line.Distant suburban traffic, streetcars, birds, resident activity, occasional car pass-bys
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it