CHILDREN ACCUSED OF PRACTICING WITCHCRAFT IN AKWA IBOM, NIGERIA: A QUALITATIVE ANALYSIS OF ONLINE NEWS MEDIA
Bibliographic record
Abstract
This essay analyzes online news media reactions to the labeling and stigmatizing of children as witches in Eket, within the state of Akwa Ibom, Nigeria. The paper was triggered by Governor Godswill Akpabio’s August 30, 2010, appearance on CNN, during which he stated that the situation of these stigmatized children is exaggerated. This essay seeks to understand what perspectives the online news media created in response to Akpabio's interview. Three themes - the children accused, the behavior of the gatekeepers (i.e., among others, parents, guardians, religious leaders, and government officials), and the practice of witchcraft - emerge from the data. The results reveal the following: (a) the Governor is defensive and in denial, (b) the involved pastors are opportunists, and (c) the accused children are abandoned, maltreated, and sometimes murdered. Results also show that none of the analyzed online news media specifically blame the parents of the accused children; rather they blame the Governor and pastors, and specifically Helen Ukpabio. Further analysis indicates that poverty is not necessarily the root of the problem as the Governor claims. The essay recommends acknowledgement of folk belief systems in the training of gatekeepers.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".