When Media Campaigns Fail: Explaining the Factors of Civil Disobedience to COVID-19 Protocols in Nigeria
Bibliographic record
Abstract
Media campaigns on COVID-19 protocols were launched in Nigeria to reduce the spread of the virus. There was evidence to suggest disobedience to the protocols. Thus, this study investigates factors that facilitated civil disobedience to COVID-19 protocols and the implications for the management of the virus. Awareness, mediatisation and personal, cultural and societal factors constructs were formulated to measure the level of civil disobedience. Kaiser–Meyer–Olkin’s Measure of Sampling Adequacy used for the study revealed that 86.4% out of the sampled size was sufficient for testing and validating variables in the civil disobedience construct, 69.1% and 63.1% for awareness and mediatisation constructs, respectively. The constructs were subjected to inferential statistical analysis, and the variables measured at the continuous and linear relationship levels. The study found economic status and media reports as the determinants of peoples’ awareness of the virus. Provision of adequate information and exposure to COVID-related contents were dominant factors under mediatisation; social distancing and use of face masks were dominant factors of civil disobedience. This study concluded that media campaigns on COVID-19 protocols failed to achieve needed behavioural changes due to economic issues, language barriers, insensitivity of government and inadequate provision of essential amenities.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".