The Polysemy of Security Community-Building: Toward a “People-Centered” Association of Southeast Asian Nations (ASEAN)?
Bibliographic record
Abstract
Abstract This article contributes to ongoing debates on security community-building in international relations (IR) by focusing on the productive role of discursive contestation in this process. It builds on recent work associated with the “practice” turn, discourse theory, and the study of security communities in the Global South to propose a new understanding of how the diversification of security governance impacts security community-building. The article develops an original discourse-based approach that conceptualizes security community-building as a polysemic, omnidirectional, and contested process in which social agents debate the meaning of security and the boundaries of community. It applies this approach to the case of Association of Southeast Asian Nations (ASEAN) to show how contestation over the organization's identity as a security community “in the making” takes place along two dimensions. First, different (and potentially incompatible) versions of the community compete for dominance. Second, contestation also unfolds “internally,” among social agents who agree on which version ought to prevail. I illustrate this part of the argument through an examination of the debate over ASEAN's identity as a “people-centered” community. The demonstration is supported by the analysis of “texts” enacted in the discursive field where the security community is talked into existence, as well as interviews with practitioners.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.006 |
| 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".