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Record W2032889811 · doi:10.2307/4127391

Lives at the Margin: Biography of Filipinos Obscure, Ordinary, and Heroic.

2002· article· en· W2032889811 on OpenAlexvenueno aff
John A. Larkin

Bibliographic record

VenuePacific Affairs · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicPhilippine History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsBiographyMargin (machine learning)HistoryLiteratureGenealogyArtComputer science

Abstract

fetched live from OpenAlex

This book examines the lives of the men and women who emerged from the margins of Philippine society to mobilize a mass following. Instead of focusing on national heroes, this volume follows an unexplored path by studying the lives of Filipinos ordinary an obscure. Drawing on extensive field and archival research, this volume's authors treat the men and women who emerged from the margins of Philippine society to mobilize a mass following. Some may have been predators or opportunists. A few mixed cunning and violence with charisma and courage. But most acted as self-conscious agents of change who led their constituents in a struggle for social justice. Almost all failed, ending their careers marginalized, impoverished, or imprisoned. By looking at the Philippine past though the prism of their lives, we can glimpse worlds now obscured at the country's margins Cebu's underworld, Iloilo's waterfront, Muslim Mindanao, the plantations of Negros, and the villages of Central Luzon. This innovative collection allows a fuller view of the processes of change and raises new questions about the character of the Philippine polity. The state's capacity to compromise or marginalize the popular resistance revealed in these biographies indicates an extraordinary resilience, a supple power, and raises doubts about the dominant view of the Philippines as a weak state. Distributed for the Center for Southeast Asian Studies, University of Wisconsin-Madison

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.224
Teacher spread0.205 · 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.

Study designNot applicable
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

Citations28
Published2002
Admission routes1
Has abstractyes

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