The organization of ideological discourse in times of unexpected crisis: Explaining how COVID-19 is exploited by populist leaders
Bibliographic record
Abstract
Using the persecution of Muslims in India that is currently taking place against the backdrop of the COVID-19 global pandemic as an illustrative case, this essay identifies the dynamics of the organization of ideological discourse by populist leaders in times of unexpected crisis. The organization of ideological discourse represents strategic, discursive acts committed by populist leaders aimed at foregrounding social conditions that would function in the advancement of various political ends—whether those ends may be the consolidation of power, the undermining of institutional systems of checks and balances, the implementation of exclusionary or injurious policies against disenfranchised constituents, the suspension of civil liberties, or a combination thereof. It is engendered through a three-stage process. In the first stage, surface-level validation by legitimate institutional actors confirms preconceived ideas about a constructed enemy. In the second stage, inflammatory rhetoric is deployed by populist leaders, which scapegoat that constructed enemy. These two stages culminate to create widespread moral panic in society. With moral panic firmly established, in the third stage an environment of fear and paranoia becomes susceptible to the enactment of symbolic and physical violence against the constructed enemy. The essay concludes with some words on the pressing need to deconstruct ideologically motivated discourses related to COVID-19.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.053 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| 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".