Bibliographic record
Abstract
In their volume The American Political Economy, Jacob S. Hacker et al. seek to renew the study of American political economy (APE) through a direct engagement with other areas of political science, including and especially comparative political economy (CPE). In the introduction of their volume, they lay out the foundations of APE as both a field of research and an approach to American politics that seeks to contribute to the study of the United States as well as to the broader discipline of political science. In this review essay, I will discuss the APE as an intellectual project to stress its key assumptions and its potential contribution to the study of politics and public policy, in the United States and beyond. Then, I will discuss two key issues that, while not explicitly central to APE as developed in The American Political Economy, could help enrich this novel approach. These two issues are policy feedback, a concept already prominent in the institutionalist tradition APE draws on, and the role of ideas in politics, which in recent decades has gained more currency in the study of politics, public policy and CPE.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".