Reading risk: The practices, limits and politics of municipal bond rating
Bibliographic record
Abstract
This article contributes to the growing literature on the relationship between local governments and financial markets by demystifying the municipal bond rating process. Since the Great Recession, the dynamics of municipal debt have moved to the forefront of American urban politics and the pursuit of high bond ratings has become a key mechanism constraining local policy autonomy. However, we know little about the actual practices of the rating agencies. Drawing on interviews conducted within these organizations, the article examines the criteria, processes and organizational practices that produce ratings. In conversation with calls for bridging the divide between political economy and techno-cultural approaches to markets, the paper shows how bond rating is structured by an ever-present tension between the need to understand localized investment risk in all its place-specific complexity while striving to fit that risk within the standardized grid of the rating scale. Further, the analysis highlights the imperative of budget flexibility through which indebted local governments are pushed into self-disciplining of their finances. As such, the article unpacks a particularly opaque area of local politics and furthers ongoing conversations between financial geography and urban political economy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".