Emerging models of networked industrial policy: recent trends in automotive policy in the USA and Germany
Bibliographic record
Abstract
The adoption of the US-Mexico-Canada (USMCA) trade agreement and the transition to electric and autonomous vehicles has created uncertainty for automotive companies. In response, the need for government efforts to position traditional automotive regions as a source of high-quality, green vehicles is pressing. The policy mix is changing rapidly as the public sector and firms cope with the challenges associated with new trade confrontations and disruptive technologies. The article captures this evolving policy landscape through a comparative analysis of automotive policy with respect to BEVs in the USA and Germany. It examines how innovation policies help the sector navigate the current technological transition. We find that theories grounded in traditional comparative political science do not provide an adequate framework to explain the observed similarities and differences in policy trajectories in the two countries. The article adopts insights from the networked industrial policy perspective to better understand the repertoire of policy instruments adopted to manage the changing impact of alternative energy technologies in the automotive industry.
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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 | 0.005 | 0.001 |
| 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".