Bibliographic record
Abstract
Charles T. Adeyanju. Deadly Racism, Disease, and a Media Panic. Halifax-Winnipeg: Fernwood Publishing, 2010. 132 pp. $17.95 sc. There are several things the audience should know before reading this book. First, a commitment to critical theory provides a key reminder: References to may not be real in the empirical/objective sense of the concept; nevertheless, the concept is real because people act as if it was real, with sometimes catastrophic consequences, thus confirming W. I. Thomas's prescient notion that unreal' (symbolic) phenomena can yield real (objective) effects. Second, mainstream newsmedia are racialized--not because of systematic (deliberate) racism--but because this coverage is systemically (unintended consequences) biasing, thanks to the predominantly negative framing of diversities and difference implicit in a prevailing media gaze. Third, the centrality of framing as a process for organizing information. Framing as persuasion draws attention to some aspect of reality as normal and acceptable, yet away from other dimensions of reality as irrelevant and inferior, in the process encouraging a preferred reading without reader/viewer awareness of their complicity or of the biases at play (hegemony'). Fourth, the concept of media hype and moral panic. Newsmedia are prone to exaggerate and sensationalize incidents or events because it's in their institutional nature to do so, often for self-serving reasons. This amplification of scare stories is not without consequences for spooking the general public into panic mode and politicians into hasty decisions. Once equipped with this knowledge, Deadly Fever begins to take shape as an empirically informed and theoretically valuable book. Much of the content and argument can be gleaned from perusing the backcover and preface. In early February 2001, the Hamilton Spectator published an article linking (erroneously as it turns out) a hospitalized Congolese woman with the possibility of importing into Canada a deadly infectious disease known as Ebola. As the author and others note, there is a long history of associating disease with and nationality (SARS or bird flu as Chinese diseases, HIV/AIDS with Haiti), in effect demonstrating the subtle and not-so-subtle ways in which the intersection of race, nationality, and gender are played out in those contemporary societies espousing a code of without race Although the subsequent media frenzy was seemingly disproportionate to the threat, the author concludes, excessive media coverage of the panic served as a proxy (subtext) for expressing white Canadian anxieties over the growing presence (and perceived menace) of racialized minorities. Clearly, then, remains a key organizational principle in framing reality along the lines of what is normal, acceptable, and desirable. After all, over-the-top reaction to the nonEbola case could resonate meaningfully only with a racialized audience already inured to/by race-logic for making sense of the world (13). The implications of this subliminal yet racialized coverage are consistent with what Frances Henry and Carole Tator call democratic racism--a uniquely Canadian racism that thrives on exploiting the contradiction between Canada's ideals of inequality and the reality of racialized inequality. …
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".