Back-shoring or re-shoring: determinants of manufacturing offshoring from emerging to least developing countries (LDCs)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Offshore outsourcing is mainly the flow of tasks from developed to emerging country firms in search of low cost production facilities. Many of these firms are now shifting their outsourcing activities from emerging to least developing countries. This paper shed light on determinants of firms based in emerging countries’ decision on shifting their outsourcing to least developing countries and to what extent it differs from developed country firm’s offshoring to emerging countries. Survey based data collected from offshoring client firms based first in South Korea and Taiwan and then engaged in re-shoring their outsourcing activities to Bangladesh and data was analyzed by multiple regression analysis. The current study found that offshoring firms enter into re-shoring to least developing countries to avail cost advantages; to have access to supplier capabilities and to focus more on strategic activities as well as to reap advantages from the institutional policy oriented advantages available in least developed countries. The findings revealed that several production factors have effects on firm’s re-shoring decision to LDCs. Transferring offshoring to LDCs and exporting from there to the developed country markets, the offshoring creates the global production network (GPN) integrating developing, emerging and developed countries.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it