Bibliographic record
Abstract
Southeast Asia is a vast region, comprised of eleven countries and incredible diversity. From one country to the next, dominant languages vary, religious groups are different and histories are all dissimilar from one another. In comparison to Latin America or Africa – other large regions of the world – the study of politics in Southeast Asia can be particularly challenging. Latin America and Africa are also very diverse regions but their respective countries share some similarities that make comparisons somewhat more common. The Spanish language, for instance, binds countries of Latin America where it is dominant in all countries except Brazil. Countries of the region were all colonized, and Spain was the dominant power for several centuries. Latin American countries inherited societies in which descendants of Spanish colonizers and mestizo (mixed) classes are now dominant. These common characteristics often tainted their style of politics, with some very interesting parallels among several countries. To a lesser extent, the African experience also generated similarities that have been compared analytically. In Africa, the division of the continent between mostly French and British colonial rule created some homogenizing experiences as well. French and English became common languages of communication throughout West and East/Southern Africa respectively. Colonization by these powers, which imposed bureaucratic structures over societies mostly organized in small political units, created some similar dysfunctionalities that have persisted in the modern independent states (Mamdani, 1996; Young, 1994). Comparisons have often been made between clusters of African countries, where the continued legacies of colonial rule have been blamed for the inability of states to overcome poverty and other major challenges in the continent.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| 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".