The Socio-Economic Dimension in Singapore's Quest for Security and Stability
Bibliographic record
Abstract
ingapore may have received kudos from the international community for charting many firsts, particularly on the economic front, but the fact remains that the republic is indeed a very small island-state and a state perpetually haunted by its sense of vulnerability. The country is merely 640 sq. km. in size and has a population of only 4 million people. Having no natural resources and comprising a predominantly ethnic Chinese populace in a region of the Malay world further constrains the republic's domestic and foreign policies.' Hence, the PAP (People's Action Party) government has adopted a pragmatic ideological culture since independence in 1965 as a way of quickly adapting to changing challenges and thereby mitigating such limitations and other anxieties confronting the city-state. Such is also its predisposition and mindset when dealing with the new security environment post-cold war and this is even more pronounced against the backdrop of globalization, in which Singapore has immersed itself fully. Evidently, there has been some perceptible change in the republic's management and approach in dealing with defence and security issues, particularly in more recent years. Perhaps this was precipitated by a similar shift in the international arena from the traditional state-centred and
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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 | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".