The Politics of Air Pollution: Urban Growth, Ecological Modernization and Symbolic Inclusion
Bibliographic record
Abstract
The Politics of Air Pollution: Urban Growth, Ecological Modernization and Symbolic Inclusion, George A. Gonzalez, Albany: State University of New York Press, 2005, pp. viii, 144. Air pollution in the United States, and especially in Los Angeles, has been widely covered in academic studies throughout the second half of the twentieth century, and the new century has already produced several new publications on the topic. Some of this abundance has to be explained by the tenacity of the problem, especially of urban air pollution and the continuing inability of state agencies and business to deal with it. In an era of Kyoto and in the face of an American federal government that is dragging its feet on the larger questions of climate control, air pollution has been recast from a nuisance to a public health problem to a global environmental threat, precisely at a time when so little real action towards its solution seems to be taking place. Yet, the continued interest in air pollution also can be explained by the blossoming of new political theories on the making of policy, which allow for reinterpretations of the data at hand (discourse theory, ecological modernization, and so on). Gonzalez's book is predominantly motivated by that second set of reasons. This long essay—it is a short book which reads more like an extended journal article—makes a distinctive argument which attempts to challenge some of the traditional stories told about air pollution regulation as well as the constructs of political thought that undergird them.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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 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".