Bibliographic record
Abstract
As with conducting an orchestra, directing the “sections” of vehicle noise pollution advocacy and hitting the right notes can be a challenge. Noise comes from a variety of sources and can be perceived differently by different people. And while the conductor tries to reconcile incompatible voices, the notes of regulation and legislation can be heard in the background, further complicating the piece. In Canada, No More Noise Toronto has initiated change at City Hall in a very short time. The grassroots campaign has been addressing high-volume vehicle noise by collecting data, critiquing existing processes, and encouraging Toronto citizens to participate, which has not been done previously. Using meters and crowdsourced methods, No More Noise Toronto seeks to understand noise “from the bedroom window,” which validates citizens’ noise reporting stories. Reviewing findings related to processes that lead to inaccurate data, barriers to involvement in the civic engagement process, and challenges created by frustration and feelings of apathy, we discuss actions taken and practices put into place as we’ve promoted a collaborative approach with stakeholders to find solutions and common ground in the shared goal of protecting the health of Toronto citizens.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".