Hate Spin: The Manufacture of Religious Offense and Its Threat to Democracy
Bibliographic record
Abstract
Description: Much of this upheaval can be traced back to the media. The common misperception is that the ‘Global Village’ envisioned by Marshall McLuhan (McLuhan and Powers, 1989) was a utopian place where the industrialized North and the global South would be joined in a shared worldview forged through modern communication technologies (‘Global Village’). In fact, the Canadian futurist saw electronic media creating not a shared worldview, but rather a ‘retribalization’ that would transform ‘the family of man into a new state of multitudinous tribal existences’, a situation that would sever ancient loyalties and was ‘far more likely’ to produce ‘conflict and discord’ than ‘uniformity and tranquility’ (Norden, 1969). As McLuhan (1977) told a Canadian television interviewer, ‘The global village is a place of very arduous interfaces and very abrasive situations’. Scrolling through social media feeds half a century later, it is evident that ‘conflict and discord’ and ‘very abrasive situations’ have become the norm. Around the world, the societal narrative has been hijacked by extremists of warring digitally mediated tribes. Electronic media is their weapon of choice. WhatsApp messages in India have sparked mob violence and lynchings (Frayer, 2018). A bogus anti-refugee YouTube video that went viral in Europe has fanned xenophobia (Funke and Mantzarlis, 2018). In the Middle East, mainstream and social media have been weaponized in a virtual confrontation that has split the Arab world (Pinnell, 2018).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".