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Record W2093049733 · doi:10.1080/13623690208409652

Guns, health and the exploitation of natural resources

2002· article· en· W2093049733 on OpenAlexaff
Owens Wiwa

Bibliographic record

VenueMedicine Conflict & Survival · 2002
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsRefugeeMultinational corporationEthnic groupPopulationSocioeconomicsGeographyNatural resourceCriminologyEconomic growthPolitical scienceEnvironmental healthSociologyLawMedicineEconomics

Abstract

fetched live from OpenAlex

There are about 1 million small arms in Nigeria, which contribute to a large number of politically motivated killings. These are mainly sectarian (Christian and Muslim) and ethnic, over land and water rights and the relationship between the activities of companies and the environment. In the Ogoni region of the Niger Delta, with a population of half a million in an area of just over 400 square miles, there are 100 oil wells; the local community is attempting non-violently to clean up their environment. From July 1993 to April 1994 there were about 3,000 cases of gun violence, compared with only two in the previous five years. There were 250 deaths and about 100 amputations. Other forms of violence, including rape, also increased. Routine procedures in the hospitals that remained open were disrupted, there were serious effects on the mental health of the community and many became refugees. It appears that the guns used in this episode were imported by a multinational company for use by the Nigerian police.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.132
GPT teacher head0.416
Teacher spread0.284 · 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

Citations4
Published2002
Admission routes1
Has abstractyes

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