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Record W2324694536 · doi:10.2307/4127345

Female Leadership of Democratic Transitions in Asia

2002· article· en· W2324694536 on OpenAlexvenueno aff
Mark R. Thompson

Bibliographic record

VenuePacific Affairs · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsDemocracyPolitical sciencePolitical economySociologyPoliticsLaw

Abstract

fetched live from OpenAlex

t is striking how often, over the last decade-and-a-half, women have led successful popular uprisings against dictatorships in Asia. Corazon C. Aquino in the Philippines (1986), Benazir Bhutto in Pakistan (1988), Khaleda Zia and Sheikh Hasina Wajed in Bangladesh (1990) and Megawati Sukarnoputri in Indonesia (1998) inspired and organized mass protests against non-democratic regimes. They then guided precarious transitions to democracy. Aquino was the Philippines' first president after the Marcos dictatorship. Bhutto served twice as prime minister in the post-Zia era in Pakistan. Khaleda Zia and Sheikh Hasina have alternated as prime minister since the end of military rule in Bangladesh. Megawati, who was initially elected vice president, succeeded to the Indonesian presidency after accusations of corruption and mismanagement led the upper house to dismiss Abdurrahman Wahid from office in July 2001. Moreover, women currently lead two democratic movements involved in ongoing struggles against authoritarianism. In Burma (which the military dictatorship has renamed Myanmar), Aung San Suu Kyi remains the country's most important oppositionist despite the 1998 massacre of protesters, the junta's refusal to recognize her party's overwhelming victory in the May 1990 elections, and her long house arrest. In Malaysia, Wan Azizah Wan Ismail leads a new opposition party and was a major figure in the opposition 199899 reformasi movement that attempted to unseat the long-reigning prime minister Mahathir Mohamad.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.006
Scholarly communication0.0060.003
Open science0.0010.009
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0170.002

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 head0.073
GPT teacher head0.260
Teacher spread0.187 · 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 designObservational
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

Citations92
Published2002
Admission routes1
Has abstractyes

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