Exports of High Technology Products from Developing Countries: Is it Real or a Statistical Artifact
Bibliographic record
Abstract
This paper first develops a consistent time-series data on the exports of high technology products from essentially the developing countries. An analysis of the data shows that developing countries are increasingly becoming exporters of manufactured products as against primary products in the past. Second the world trade is increasingly becoming a trade in high tech products. What is more striking is the significant increase of the technology content of exports by developing countries: very nearly a quarter of the exports from developing countries is now in high tech products. Third, the share of developing countries in high tech exports have shown dramatic increases: it has increased from about 8 per cent in 1988 to about 23 per cent by 1997. But there is considerable concentration of it in a few countries. The paper then seeks to explain whether these developing countries are real exporters of high tech products or not. This is accomplished by a careful examination of the degree of product specialisation by both developed and developing countries, by examining their record with respect to patenting and finally by analysing certain indicators of high tech competitiveness. The paper concludes by presenting a case study of a leading high tech exporter from the developing world. Key Words: Exports; High-technology; Innovation; Developing Countries; Competitiveness JEL classification: N70, O34, O38, O53 CONTENTS 1.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".