Proposing a Framework to Extend the Global Commodity Chain Theory: A Case Based Study with Evidence from Garment Supply Chain
Bibliographic record
Abstract
The traditional Supply Chain Management Theory has been around for few decades. In addition, between 1994 and 2000, another theory by the name of the ‘Global Commodity Chain’ (GCC) theory was developed by Gary Gereffi from North Carolina University (USA) which is more broader than the Supply Chain Management Theory. The aim of this paper is to revisit and critically examine Gereffi’s (1994) GCC theory and attempt to expand its analytical framework from the perspective of a small island country in the Pacific. The research findings highlight some of the limitations which GCC theory and suggest that a full understanding of global commodity chains needs to be reframed and embedded in the context of a country’s national social, economic and political environment. The paper argues that GCC theory need to incorporate variables such a as of ‘national economic policies’, ‘role of state’ and ‘labor’ in order to fully account for the complexity of modern supply chains. The paper concludes by arguing that the GCC theory is limited in explaining the true picture in developing small island countries. The paper contributes literature on GCC theory.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| 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".