Context and Comparison in Southeast Asia: The Practical Side of the Area Studies-Discipline Debate: A Response to the Special Issue of "Pacific Affairs": "Context, Concepts, and Comparison in Southeast Asian Studies" (Vol. 87, No. 3)
Bibliographic record
Abstract
A recent Pacific Affairs special issue explores key dimensions of the discipline/ area studies divide in the context of Southeast Asia. It asks whether it is possible to use the comparative methods favoured by disciplines while doing justice to the rich nuance of individual cases. We offer a practical perspective on this debate. We argue that the demands of discipline audiences and area-studies audiences can vary significantly, making it difficult to effectively address both within a given project. Furthermore, while individual scholars retain agency over the nature of their research, structural factors like the job market and tenure requirements nudge junior scholars towards disciplinary audiences. We support this claim with an analysis of several academic job markets across the social sciences and humanities. We also interview several junior scholars who focus on Southeast Asia to examine the channels that link structural factors with scholarly orientations, finding both direct and backchannel connections. We conclude that in the absence of structural changes to the hiring and promotion practices at major universities, the question of an ideal balance between comparative approaches and deep area nuance will be answered by practical—rather than ontological or normative—concerns.
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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 | 0.074 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.015 | 0.040 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".