Analysis of Government Procurement Market in Emerging Countries and Implications for Small and Medium Enterprises
Bibliographic record
Abstract
The purpose of this study is to derive implications for SMEs through analyzing the openness of the government procurement market in emerging countries the prospects of these markets opening up in the future, and the overall possibility and plans of Korean SMEs to enter these markets. The procurement markets of international organizations and advanced countries are already saturated with competition, and it is difficult to expand in the procurement market of advanced countries such as the United States, Canada and the EU as they are strengthening their preferential purchasing system in line with protectionist trade policies. Therefore, this study aimed to find a market for competitive companies in Korea by comprehensively grasping the current situation, openness, growth potential, and potential market demand of emerging markets. In particular, we focused on analyzing the possibility of Korean SMEs advancing into the government procurement market of emerging countries, and to offer suggestions on the direction of procurement policies and trade policies in order to effectively advance into these emerging markets. This study analyzed six emerging regions which are either considering WTO-GPA membership or negotiating FTA agreements with Korea: China, Southeast Asia and India, Eurasia, the Middle East, Middle Eastern Europe and Latin America.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".