Bibliographic record
Abstract
Resource interdependence has been a major factor contributing to economic integration in the Asia-Pacific region during the post-war period. First driven by Japan’s high-speed growth of the 1960s, and then followed by the industrialisation of other Northeast Asian economies (Korea, Taiwan and China), the demand for mineral resources from the region’s industrial centres — in particular their steel sectors — has been steadily growing. But unlike the experience of western nations earlier in the century, an almost total lack of local reserves of minerals and energy forced these Northeast Asian economies to look to foreign sources of mineral resources from the outset of their industrialisation programmes. Such outward dependence for mineral resources fostered new mining industries in a number of countries on the Pacific Rim — namely Australia, Brazil and Canada — which were specifically developed to service demand for coal and iron ore from Northeast Asia’s growing steel industries. As a result of their mutual interdependence, the fates of these two industries became intertwined, coming to form a set of functionally-integrated global production networks (GPNs) that connect Asia’s mining and steel industries through resource trade and investment ties. These resource production networks have since played a major role in the economic development of all the involved countries — with steel underpinning the heavy industrialisation associated with high-speed growth in Northeast Asia, and mining acting as the leading export earner for regional mineral supplier economies. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.340 | 0.173 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".