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Record W3008903256 · doi:10.5430/ijhe.v9n2p270

Prevalent Crime in Nigerian Tertiary Institutions and Administrative

2020· article· en· W3008903256 on OpenAlexvenueno aff
Romina Ifeoma Asiyai, Enamiroro Patrick Oghuvbu

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHarassmentStratified samplingTertiary institutionInstitutionSample (material)Cronbach's alphaSocioeconomicsPopulationCrime preventionCriminologyGeographyBusinessPsychologyMedical educationDemographySocial scienceSociologyMedicineSocial psychologyService (business)Marketing

Abstract

fetched live from OpenAlex

This study examined crime in tertiary institutions in southern Nigeria. The purpose of the study was to identify the common crime prevalent in the institution, the administrative strategies for managing crime and find out the extent to which institution managers or Administrators are employing the identified strategies to ensure a crime free environment. Three research questions were asked and answered. The sample of the study was drawn from a population of tertiary institutions in Southern Nigeria A sample of 1020 respondents were selected through stratified random sampling technique from nine tertiary institutions in South-West Nigeria. The questionnaire was the instrument for collection of data from the respondents. It is divided into three sub-scales. Cronbach Alpha for the three sub scales yielded 0.84, 0.80 and 0.76. The finding revealed the common crime prevalent in tertiary institution in South-West Nigeria as examination malpractices, assault, plagiarism, sexual harassment, and certificate forgery. The findings further showed that crime management strategies like mounting closed circuit camera in strategic locations, regular monitoring of activities, use of anti-cult group are some of the identified crime management strategies. The extent of employment of the identified strategies in crime management is low.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.211

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.366
Teacher spread0.310 · 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.

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

Citations7
Published2020
Admission routes1
Has abstractyes

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