Bibliographic record
Abstract
This paper seeks to examine whether Malaysia is facing negative de-industrialization by examining value-added, trade, and productivity trends over the period 1990-2005. The evidence produced in the paper is concrete enough to confirm that Malaysia is facing negative de-industrialization. While it is typical, as part of the process of structural change, to see a rise and fall in the share occupied by manufacturing in the GDP, the evidence shows that Malaysia is indeed facing premature de-industrialization, with a trend slowdown in manufacturing value-added, trade performance, and productivity since 2000. Not only has the trade performance of manufacturing been falling, manufacturing labor productivity has also slowed down; with the key sectors, such as electric-electronics, textiles, and transport equipment; showing either negative or low productivity growth since 2000. Malaysian industrial policies have been fairly successful in connecting with the global value chains of multinationals and in developing resource-based industries, but have not achieved the same success in stimulating their transformation to high value-added activities. The lack of effective institutional change, partly explained by ethnic policies, is advanced as the prime reason for the setting in of negative de-industrialization in Malaysia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".