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Record W4403667453 · doi:10.5509/2025981-art1

Perspective Contours of Precarity: A Perspective on Vulnerability and Insecurity in Southeast Asia

2024· article· en· W4403667453 on OpenAlexvenueno aff
Paul J. Carnegie

Bibliographic record

VenuePacific Affairs · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSoutheast Asian Sociopolitical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPrecarityVulnerability (computing)Perspective (graphical)Southeast asiaEconomic geographyGeographyDevelopment economicsPolitical scienceSociologyGender studiesEconomicsEthnologyComputer security

Abstract

fetched live from OpenAlex

Southeast Asia is experiencing high levels of accumulation, extractive activity, speculation, infrastructural development, and indebtedness. At the same time, state-business-investment agendas regularly downplay issues of marginality and disadvantage. This paper provides a perspective on vulnerability and insecurity in the region. Drawing on observations over a number of years, combined with a review of relevant literature and the use of illustrative examples, it details how and why the concept of precarity is particularly relevant for understanding contemporary forms of jeopardy in Southeast Asia. What follows is the case for a grounded and disaggregated precarity perspective to decipher the less acknowledged forces and interests shaping circumstances we seek to prevent. Rather than relying on limited frames of reference, such an approach can bring greater awareness to localized distress stemming from untrammelled accumulation, speculation, extractive activities, and indebtedness. This conceptual shift is arguably crucial for effectively understanding and mitigating vulnerabilities. If states in Southeast Asia continue to disregard the significance of the interplay between politics, commercial interests, and development agendas in driving ecological degradation, social disadvantage, and marginalization, then the likelihood is a precarious future for many.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.328
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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