Asian Drivers: Opportunities and Threats
Bibliographic record
Abstract
Not for the first time, real events have caught the research community by surprise. From the late twentieth century Asian economies began to play an increasingly important role as global producers, beginning in Japan after the 1960s, and spreading to some other East Asian economies during the last quarter of the century. At the dawn of the new millennium, the momentum of Asia has been significantly strengthened by the very rapid growth of two very large economies: China and India. But this time, so significant is the rapidly unfolding entry of these countries into global markets, that the policy costs of the research-gap are particularly high. Hitherto, recognition of the consequences of the rise of these newly dynamic Asian economies (The Asian Drivers) has almost entirely been confined to the high-income economies. But what are the impacts likely to be on poor countries, and poor communities in poor countries? How might the opportunities opened-up by the rise of the Asian Drivers be grasped and the threats which they pose be minimised for those countries which comprise the majority of the world’s population, but account for a minority of global GDP? In November 2004 and May 2005, a team of researchers from IDS met with various networks of researchers to begin outlining a networked and policy-focused research programme addressing the impact of the Asian Drivers on low-income economies. This IDS Bulletin draws on participant contributions to advance the research agenda. It questions the impact that Asian Drivers will have on the developing world, a particularly apposite point given the commitment of the global community to halve the incidence of global poverty by 2015.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".