Bibliographic record
Abstract
Like many cities, the City of Ottawa got interested in climate change early on. It set ambitious targets, only to realize they were extremely demanding if not impossible to meet. It thus scaled back its ambition, and the history of its response at the overall level of the City Council can be characterized in two ways. On the one hand, it responded to the emergence of city networks on climate change to do enough to report to the network of Canadian cities – the Partnership for Climate Protection – and meet its expectations. But these expectations have throughout been minimal – to create inventories of emissions, develop plans for them, and monitor progress. On the other hand, the City has, episodically, generated moments of enthusiasm for action on climate change, developing more overarching plans and a variety of specific initiatives. These bursts of energy occurred around 1991, 2004, and 2012–13. But in none of them did the burst of energy turn into sustained attention. We focus our analysis on the period through to 2014 when the Air Quality and Climate Change Management Plan (AQCCMP) was adopted. 1 In part this is because the main patterns are easily identified up to this date, and in part because Chapters 5 and 6 discuss two particular aspects of the more recent politics (Complete Streets, and intensification) in more detail.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".