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Record W1991871780 · doi:10.2307/4127282

Law and Order in a Weak State: Crime and Politics in Papua New Guinea.

2002· article· en· W1991871780 on OpenAlexaffvenue
John Barker

Bibliographic record

VenuePacific Affairs · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNew guineaPoliticsState (computer science)Order (exchange)Political scienceLawCriminologySociologyEthnologyEconomicsMathematics

Abstract

fetched live from OpenAlex

Twenty-five years after independence, Papua New Guinea is beset by social, economic, and political problems: poverty and inequality, a young and expanding population, a stagnant economy, corruption, and rising crime. This book examines these problems of order in light of Papua New Guinea's remarkable social diversity and the impact of rapid and pervasive processes of change.Three original and strategic case studies involving urban gangs, mining security, and election violence form the core of the work. Each case study looks at particular forms of conflict, and the responses these engender, across different socioeconomic contexts and geographic locations. Empirical data are analysed through a common framework that employs material, cultural, and institutional perspectives, allowing readers to view the three cases through different theoretical prisms, identify link-ages between them, and, in the process, build a larger picture of the post-colonial social order.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.008
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.002
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 head0.255
Teacher spread0.236 · 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

Citations103
Published2002
Admission routes2
Has abstractyes

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