Newspaper Framing of Gender-based (domestic) Violence of Women –on-Men from Nyeri County, Kenya
Bibliographic record
Abstract
This paper examined print media representations of gender-based domestic violence messagesfrom Nyeri, Kenya, one of the counties inhabited by the Kikuyu ethnic group. The Kikuyu areKenya‘s most populous ethnic group – this is according to the 2009 Kenya population census(Basse, 2010). Two newspapers, the Daily Nation (mainstream) and the Nairobian (tabloid-style)weekly provided the data for the study. Content analysis was used to examine the frequency offrames, prominence, type of stories, and sources used in the stories while critical discourseanalysis (CDA) helped examine emergent themes. Purposive sampling was used to select newsarticles on gender-based domestic violence in Nyeri County published by the two newspapersbetween June 1st, 2015 and August 31st, 2015. In total, 22 articles were analyzed from bothnewspapers. The main findings: (1) Most of the news articles had a negative tone: Daily Nation(eight) and the Nairobian (nine); (2) the Nairobian covered the domestic violence in Nyeri in asensational manner using vivid language, graphics and colorful pictorials, while the DailyNation used a conservative approach in its coverage; (3) The two newspapers framed the Nyeriwoman as an angry, violent and dangerous woman while the Nyeri man was framed as mainlyan alcoholic and helpless victim; and (4) Previous gender media narratives such as the Bobbitt‘sgender violence story and the Angry black woman phenomenon parallel the localizedNyerification effect.Key Words: Nyerification, Gender-Based (Domestic) Violence, Stereotypes, Media Framing,Content Analysis, Critical Discourse Analysis
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".