The Work of Policy: Actor Networks, Governmentality, and Local Action on Climate Change in Portland, Oregon
Bibliographic record
Abstract
To develop and implement public policy requires work. In this paper, we examine some of the work involved in a pathbreaking climate change policy adopted in Portland, Oregon. Seeking to address shortcomings in existing studies of local environmental governance, we focus particular attention on how climate change became a political priority in Portland, how a particular representation of local carbon dioxide emissions was developed in the process of public consultations, and how the local state attempted to achieve its adopted policy objectives by enlisting the self-governing capacities of its residents. To carry out such an analysis, we draw on both actor-network theory (ANT) and governmentality. The first approach offers an understanding of how collective priorities emerge as different actants learn how to move toward their goals by working together, and also suggests how subjects and objects are reshaped by their enrolment in such configurations. The second approach offers a more precise understanding of how the state attempts to achieve its objectives—once they are established—by conducting the conduct of its citizens. Brought together, we argue, ANT and governmentality provide an incisive approach to questions of local environmental governance, and to broader political concerns as well. As each approach addresses well-cited shortcomings of the other, the combined approach developed in this paper could be deployed in many studies that examine the emergence of political priorities and the capacity to achieve them.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".