A Short Guide to Thinking About Industrial Policy: Takeaways from the New Economics of Industrial Policy
Bibliographic record
Abstract
The recent return of industrial policy has inspired a new economic literature on industrial policy. This chapter summarizes six high-level insights from this nascent literature. (1) Properly defined, industrial policy is a vast space. (2) Its use is widespread and on the rise. (3) Given the breadth and extent of industrial policy, sweeping, binary claims about it are unsustainable. (4) The emerging empirical picture is complex and should be. (5) Political economy is first order, and institutional details matter as much as technical details. (6) We should not discount the potential of smaller, contemporary transformations; best practices and policy lessons are likely in our backyard. We point to some recent experiences in developing countries. These takeaways are not exhaustive and point to a more nuanced, pragmatic body of knowledge.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.015 |
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".