The Philippines in the Electronics & Electrical Global Value Chain
Bibliographic record
Abstract
This report uses the Duke CGGC global value chain (GVC) framework to examine the role of the Philippines in the global electronics & electrical (E&E) industry and identify opportunities to upgrade. Electronics and electrical equipment have played an important role in the Philippine economy since the 1970s and form the foundation of the country’s export basket today. In 2014, these sectors accounted for 47% of total exports from the Philippines at US$28.8 billion, of which 41% was from electronics, and 6% from electrical products. From a global perspective, while the Philippines is not the leading exporter in any particular product category, it is known for its significant number of semiconductor assembly and test (A&T) facilities. The global economic crisis (2008-09), combined with the exit of Intel (2009), had a significant negative impact on electronics exports and, although steadily increasing, they have not yet rebounded to pre-crisis levels. Nonetheless, investment in the E&E industries has picked up since 2010; in the past five years, there have been 110 new investments in these sectors. Another positive sign is the low exit rate; with the exception of Intel, companies that have invested in the Philippines have stayed, with several operations dating back to the late 1970s and 1980s. These firms have not only stayed, but have continued to grow and expand in the country due to the quality of the workforce and satisfaction with the Philippine Economic Zone Authority (PEZA) environment. The growth of the industry has significantly benefited from foreign investment and close ties with Japanese firms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".