Family Backgroun and Corrupt Practices in Bayelsa State, Nigeria
Bibliographic record
Abstract
The study investigated family background and corruption nexus in selected Local Government Areas in Bayelsa state. The correlational design was adopted for the study. With Taro Yemane formula, the study sampled a total of (400=100%) respondents. Data for the study was gathered through structured questionnaires.However (203=50.75%) copies of questionnaires were retrieved from eight selected communities. Cronbach Alpha was used to determine the reliability of the research instrument. Both Probability (simple random, cluster) and non-probability (purposive, accidental) sampling techniques were adopted for sampling procedures. Data for the study were analyzed with univariate and multivariate statistics with the aid of Statistical Package for Social Sciences (SPSS) version 23.0. The study found that defaulting family background led to corrupt attitude among children during adulthood. Telling lies was the major corrupt attitude reported in the study. Parental irresponsibility led to sociopathic tendencies which influence corruption among others. Based on the findings, the study recommends family restricting, national juvenile re-orientation, monitoring of unwanted pregnancies, enforcing of swift punishment on individuals found guilty of corruption 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".