Bibliographic record
Abstract
As anti-globalization and geopolitical tensions continue to rise, the use of local content requirements (LCRs) around the world has become more noticeable than ever before. The reasons for adopting LCRs range from ensuring domestic supply availability, job creation, and increasing value added to safeguarding national security. Ing and Grossman examine country-specific as well as firm-product level exercises to explain how LCRs reduce fair competition, resulting in lower trade and productivity, which ultimately lowers world economic output and overall human welfare. Countries around the world are investigated with specific attention to the US, China, Indonesia, and resource-intensive countries, including mining-intensive ones. The book also presents product- and firm-level analyses, answering the question of why countries adopted LCRs and how LCRs actually affect the world economy. This book is a useful resource that will interest policymakers, researchers, and advanced undergraduates interested in international trade, industrial policy, political economy, labour economics, and development economics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.290 | 0.163 |
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".