The Othering of the Black Community in News Media Reports During the Ebola Epidemic of 2013 to 2016
Bibliographic record
Abstract
The purpose of this paper is to identify and analyze the racial undertones found in the news media reports on the West Africa Ebola outbreak of 2013 to 2016, focusing mainly on the portrayal of North American cases on television. As with many political activist issues, the first step to making a change for the better is recognizing exactly where the errors are made. Through the analysis of news reports posted by CityNews and The National, the paper identifies four critical themes: Othering, Them versus Us, and the impact of Visualization. Othering is the process of alienating the Black community from the rest of the population as the leading responsible factor for Ebola. This creates a dilemma of Them (the Black community) versus Us (the general population) that exasperates the already existing racial tensions. All of which is done not only by what is expressed by the reporters, but what is shown on the screen as part of the news story. This is evidence of systemic institutional racism in the media industry. By understanding the key reoccurring themes of racism found in the event of an epidemic, society can be better prepared to confront the situation when it arises again.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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 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".