Bibliographic record
Abstract
This introductory chapter discusses the shifting ambitions and positions of city governments. Once considered the purveyors of street repairs and sewer mains, city governments are now being heralded as innovative, entrepreneurial, and dynamic actors ready to take on societal challenges that other levels of government seem unprepared or unwilling to address. Indeed, city governments are viewed, and are viewing themselves, as able to effectively pursue major policy agendas once considered the sole purview of national governments. From labor to immigration to climate change, there has been a shift in both practice and rhetoric to cities. In the United States, city governments from Bangor, Maine, to Los Angeles, California, are raising the minimum wage for their residents, even as many state governments scramble to prevent them from doing so. The chapter explains that the book focuses on local efforts to address global climate change. It explores the means by which city governments—particularly those of New York City, Los Angeles, and Toronto—pursue climate change mitigation, or reducing the greenhouse gas emissions produced by urban systems, and to what ends.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".