Coronavirus Lockdown and Poverty in Nigeria: Implications forCrime Upsurge in Yenagoa Metropolis, Bayelsa State
Bibliographic record
Abstract
The study explored the impact of Covid-19 lockdown on crime upsurge in Yenagoa metropolis, Bayelsa state. The study adopted Robert K. Merton Anomie theory. Correlational study design was utilized. With Taro Yemane formula, the study sampled a total of (399=100%) respondents. Data for the study was gathered through structured questionnaires. However (200=50.1%) copies of questionnaires were retrieved. Cronbach Alpha was used to determine the reliability of the research instrument. Both Probability (simple random, stratified) and non-probability (purposive) sampling techniques were adopted for sampling procedures. Data for the study were analyzed with Simple Percentages, Frequencies and Chi-Square with the aid of Statistical Package for Social Sciences (SPSS) version 23.0. Data analysis indicated that Covid-19 lockdown led to the proliferation of specific crime types (armed robbery, cybercrime, burglary, human right abuse, domestic/gender violence, bribery) at different frequencies. Based on the result, the study recommended gradual easing of lockdown, provision of post lockdown palliatives, reduction in job losses by government and provision of socio-economic stimulus to cushion the effects of job loss on criminality among others.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".