Information Diffusion and Utilization of Undergraduates ‘Drug Free Clubs’ Against Drug Trafficking in Anambra State
Bibliographic record
Abstract
This study evaluated Information Diffusion and Utilization of Undergraduates Drug Free Clubs against Drug Trafficking in Anambra State. The study grew from the alarming rate at which youths are being arrested, prosecuted, jailed and executed for drug trafficking within and outside the country. It is premised against the background that youths are the most vulnerable to drug trafficking, therefore examined the information diffusion and utilization of drug free clubs against drug trafficking among youths of tertiary institutions in Anambra State from three senatorial districts of the state. Anchored on Social Cognitive theory and Diffusion of Innovation theory, the objective of the study was aimed at ascertaining the respondents’ exposure to information against drug trafficking, their source of information against drug trafficking and the attitude of the respondents to campaign message. The study adopted a survey research method with questionnaire as instrument for data collection. It also used Krejcie and Morgan formular table to determine the sample size for the population of 50, 652 students. Descriptive statistical tools such as frequencies and percentages were used in answering the research questions. Results obtained from the study showed that respondents were exposed to information against drug trafficking, their channels of exposure include drug free clubs and mass media, it was also observed that majority of the respondents benefited from information diffusion of drug free clubs from their schools. The study recommended advocacy campaigns to further discourage students from engaging in drug trafficking.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".