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Record W2970454929 · doi:10.6000/1929-4409.2019.08.09

An Appraisal of Sea Robbery Control in Nigeria’s Waterways: Lessons from Niger Delta Region

2019· article· en· W2970454929 on OpenAlexvenueno aff
Toakodi Adongoi, Otodo Ifeanyichukwu, Adioni-Arogo Azibasuam

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsNiger deltaMandateSample (material)Systematic samplingData collectionMultistage samplingSampling (signal processing)GeographySocioeconomicsEnvironmental resource managementStatisticsComputer scienceDeltaEngineeringEnvironmental sciencePolitical scienceMathematicsLawSociologyTelecommunications

Abstract

fetched live from OpenAlex

This study examined strategies adopted by relevant security agencies to curb sea robbery in Niger Delta Region of Nigeria. A multi-stage sampling technique was employed to select respondents’. A Sample of 400 was derived using Taro Yamane sample size determination technique. Questionnaires and oral interview were the methods used for data collection, while data collected were analysed using descriptive statistics. Results revealed that the joint task force (JTF) is selective in carrying out its constitutional mandate as their presence is felt only in critical facilities, which implies that security is porous and much is needed to guarantee safety of lives and properties on Nigeria territorial waters. Consequent upon this finding, crime-mapping as indicated on GIS map of sample states is recommended.

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)0.001
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 score0.062
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.051
GPT teacher head0.287
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

Citations3
Published2019
Admission routes1
Has abstractyes

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