Methods and means of export promotion : why the United States must follow Europe's example
Bibliographic record
Abstract
In the age of globalization, export competitiveness is a priority for policymakers in countries around the world. Key issues such as employment and economic growth are tied to a country's exports, and persistent trade deficits can have negative long-term repercussions. Despite its current status as the world's largest exporter of goods and services, the United States has run a considerable trade deficit for the past three decades—a deficit that soared to record heights in 2006. Meanwhile Germany, Europe's largest economy, remains the world's largest exporter of goods, and maintains a large trade surplus. As a whole, the EU has a small, but manageable, external trade deficit, and remains extremely competitive. While U.S. and European exporters are subject to different economic conditions, both must actively compete for sales in the global marketplace. The U.S., EU, and EU member states all have export promotion programs to assist their firms in this competition. While the United States makes a considerable effort to promote export activity, it falls short in comparison to Europe. Although a trade deficit does not stem solely from lackluster export promotion efforts, the United States must improve its export promotion programs as a means to address the deficit, at least in part. To this end, much can be learned from Europe.
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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 | 0.054 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".