Bibliographic record
Abstract
52 Mauritius sugar industry 195, 214 Netherlands 14, 15, 25 New Zealand forestry 222-8 Ahold 18 Aker 145 Akzo 12, 18 Amsden, A. 76 Andean Pact 185 Antwerp port 31, 44 Apple 78, 81 Austria 110, 111 banking sector, Netherlands 19 Bartlett, C.A. 156, 157 Belgium 4, 7, 30, 45 clusters 41-2 life sciences 42-5 data 35-6, 48 economy 30-35 ownership characteristics of companies 36-9 Transnationality Index (TNI) 2 Bernard, A.B. 39 biotechnology, Belgium 42-3 Brazil 186 Cambodia 7, 8competitive advantages 264-7 foreign direct investment (FDI) 244-5, 267-8 approved versus realized FDI 258-60 business environment 245-8 capital formation and 262 economic development and 260-64 employment/poverty reduction and 262-4 international trade and 261-2 origins 248-50 provincial distribution 256-8 sectoral distribution 253-6 technology transfer and 260-61 types of FDI 250-52 Canada 5-6, 154-5, 167-8 motivations for foreign direct investment (FDI) 157, 160-61 regional context 155-6 research methodology 159 subsidiary companies 157-8, 161-3 comparisons 163-5 regional integration and 159, 165-7 survey 159-67 Transnationality Index (TNI) 3 Cantwell, J.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.603 | 0.472 |
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".