Bibliographic record
Abstract
The clouds of the financial storm that raged in 1997–98 continue, metaphorically, to envelope Southeast Asia. Although the region is showing signs of financial recovery it is tentative and remains vulnerable to shocks in the international political economy. The loss of financial confidence in the American market following a series of scandals in 2002, coupled to the threat of war against Iraq, has begun to impact upon the economic recovery of Southeast Asia. Singapore, the region’s economically most secure state, recorded only a 3.7 per cent rate of growth between July and September 2002. This was considerably less than the 7 per cent estimated rate of growth for the third quarter, and whereas this quarter was expected to show a levelling out on a quarter-on-quarter basis, it actually dropped by 10.3 per cent. The literal clouds that float over the region are no less threatening. With the forest-fires on Kalimantan and elsewhere in Indonesia sending pollutants now annually into the atmosphere, the euphemistically named ‘haze’ remains a significant economic and environmental problem. On the ground a variety of transnational problems are threatening the stability of the region. These include drug trafficking, which in 1999 the Thai government identified as the number one threat to national security. Other forms of international crime (people-smuggling, money laundering), and since 11 September 2001 global terrorism, are also causing tensions in the region.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".