Bibliographic record
Abstract
In the past decade, there has been a global resurgence in attention to industrial policy (IP), a resurgence that cuts across political ideologies and geographic regions. IPs are inherently political, intimately connected to the roles of the state in the economy and of states within an international economic system. This review demonstrates that while overt IPs have waxed and waned in their political acceptability in the aftermath of World War II, IPs have always remained part of the policy tool kit. In using IP, policymakers have had to navigate three common governance domains: building coalitions to support productive investments, building the state's capacity to collaborate with and discipline the private sector, and creating political incentives for credible commitments to firms. Nonetheless, the political dynamics in each of these domains have shifted over time. Historically, IPs focused on export-based catch-up strategies, requiring the mobilization of coalitions around manufacturing investment and export discipline. Today's IPs often target frontier technologies and aim to address perceived vulnerabilities in global supply chains and new geopolitical competition, demanding greater experimentation with more uncertain economic outcomes and higher risks of failure. We trace the evolution of the literature on IP through four phases: state-led developmental policies, the changing coalitions and institutions in a globally fragmented production system, neoliberalism, and the more recent renewed focus on transformative IP.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".