Bibliographic record
Abstract
affiliates of MNEs 47, 64, 79, 102 in China 69 East Asian crisis, role in recovery FDI as percentage of GDI 112, 113 Japanese affiliates 115-16 Malaysian manufacturing 116, 117-18, 119 US affiliates, employment 115 US affiliates, exports 114 in high-tech industries 53 in India 64 in Indonesian manufacturing 116 manufacturing exports share 55-6, 57-61 R&D activities 192 see also multinational enterprises AFTA (ASEAN Free Trade Area) 88 components trade 77, 80-84 agro-based processed food 50 annual export growth 54, 55-6 Argentina 125, 130 see also Latin America ASEAN countries components trade 93 exports to china 37 FDI industries 39 FDI inflows 33-4 foreign investment regime 16-20 ASEAN Free Trade Area (AFTA) 88 components trade 77, 80-84 Asian financial crisis 14, 17, 19, 229 capital flows 102-12 Asian crisis countries 104-5 mergers and acquisitions 109 net capital flows in Asian countries 106-8 net capital flows percentage change 109 US direct investment 110, 111 crisis management policy 14, 17 FDI, effect on 33-4
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.778 | 0.684 |
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".