Contentious Implementation and Retrenchment in Neoliberal Policy Reform: The Global Electric Power Industry, 1989–2001
Bibliographic record
Abstract
We develop theory about the effect of domestic and global institutional forces across countries and over time, following a national government's adoption of a globally diffusing policy, on retrenchment—the degree to which a government reinstates the objectives of a policy's predecessor without repealing the new policy to balance conflicting institutional forces. World political culture legitimates and supports the new policy, while the policy's domestic opponents seek to mobilize opposition to it. Peer country governments' behavior and intergovernmental organizations may help or hinder domestic opponents' efforts. We tested our model by examining governments' renegotiation of the terms of private electricity generation projects in 62 countries in 1989–2001. Although no country formally repealed electricity liberalization during that period, governments selectively renegotiated the terms of private investment in roughly 20 percent of private power generation projects in countries that liberalized. Results support our hypotheses about the effects of domestic and global institutional forces—the former of which we measure through automated natural language parsing of 8.52 million newspaper articles—and the idea that domestic audiences' preexisting cognitive constructs and normative beliefs shape governments' implementation of globally diffusing policies.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".