An Appraisal of the Role and Achievements of the Asean Regional Forum, 1994-2007
Bibliographic record
Abstract
The ASEAN Regional Forum (ARF) was inaugurated on 25 July 1994 in Bangkok by ASEAN - 6 in response to the general feeling among the nations of the Asia-Pacific area that a multilateral security arrangement was timely for the region. It was the realization of the 1992 Singapore Declaration of the Fourth ASEAN Summit which had proclaimed its desire to intensify ASEAN's external dialogue in political and security matters as a means of building cooperative ties with states in the Asia-Pacific region. The first meeting was attended by 18 states as follows: The six ASEAN members consisting of Brunei Darussalam, Indonesia, Malaysia, Philippines, Singapore and Thailand; seven ASEAN's Dialogue Partners consisting of Australia, Canada, the European Union, Japan, New Zealand, Republic of Korea and the United States; two ASEAN's Consultative Partners comprising China and Russia; and three ASEAN's Observers, consisting of Laos, Papua New Guinea and Vietnam. Since its formation in 1994, the ARF has come a long way and now (2007) boasts of a membership of 27 members 2 However there is a great deal of criticism that the ARF is ineffective in organization - a "talk-shop" which has become practically irrelevant. In these circumstances an evaluation of its achievements would be most appropriate.
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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".