“Common Sense Geography” and the Elected Official: Technical Evidence and Conceptions of ‘Trust’ in Toronto’s Gardiner Expressway Decision
Bibliographic record
Abstract
In fields such as Sociology and Political Science, there have been, over the course of three decades, attempts to engage elected officials in “Evidence-Based Decision-Making”. Evidence is generally conceived as “expert” advice provided to politicians. A question that has gained more centrality in recent years is “why do elected officials not trust expert opinion or technical evidence?” and the answer to this question has been sought in historical or general terms (e.g. Irwin 2006; Weiss et al. 2008; Kraft et al. 2015). Here I will propose an alternative question: “when politicians exhibit a lack of trust in expert advice, how is such skepticism publicly accounted for?” I will examine this question by utilizing a case study ethnographic approach to the City of Toronto’s controversial decision to endorse the Hybrid alternative for the Gardiner expressway. By doing so, I intend to show that knowledge controversies are not inherently a form of deficiency on the part of the elected official – that they are ignorant to the implications of evidence – but rather the standard by which elected officials and appointed experts review and understand evidence can lead to very different (although both reasonably ‘correct’) conclusions.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.079 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".