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Recovering from Crisis: The Case of Thailand's Spatial Fix

2007· article· en· W2038096951 on OpenAlexaff
Jim Glassman

Bibliographic record

VenueEconomic Geography · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicSoutheast Asian Sociopolitical Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAppropriationCentralityDecentralizationInvestment (military)PoliticsWork (physics)Economic systemEconomic geographyEconomicsEconomyPolitical scienceDevelopment economicsMarket economy

Abstract

fetched live from OpenAlex

Abstract: Although the Asian economic crisis has been the subject of numerous analyses, the varied and uneven processes by which different Asian countries have recovered from the crisis have received comparatively less attention. This article focuses on the process of recovery in Thailand. While the crisis and recovery both have international dimensions that go beyond individual nation‐states, the case of Thailand can be used to analyze some of the forces that are at work in both the national and international contexts. Thailand's process of recovery can be analyzed by noting tensions and overlaps among different forms of spatial fix—those involving investment in Bangkok' built environment, those involving the geographic decentralization of investment to lower‐cost production sites, and those involving the effort to expand exports. Each of these spatial fixes involves different accumulation strategies and, therefore, political coalitions. This situation suggests the centrality of social struggles over the appropriation of surplus to both crisis and recovery.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0160.013
Scholarly communication0.0070.003
Open science0.0010.009
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0100.001

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 head0.012
GPT teacher head0.275
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), 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

Citations64
Published2007
Admission routes1
Has abstractyes

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