Exploring the Threat of Fake News: Facts, Opinions, and Judgement
Bibliographic record
Abstract
This article explores how fake news, variously described as misinformation, disinformation, malinformation, and post-truth threatens our pluralistic democratic life. We ask, how does fake news function in constructing a world of meaning that destabilises the conditions under which we are able to render valid political judgements in democratic life? Using the 1992 R v Zundel Supreme Court Case from Canada to explore the free speech question, and Hannah Arendt’s distinction between fact and opinion, we argue that fake news uses the malleability of language to displace fact with opinion. This displacement threatens democracy in two ways. First, fake news functions by deploying language in such a way that it is built on refuting its own ability to produce factual knowledge, and in the process the world becomes one of opinion treated axiomatically. Second, as a consequence, it renders judgement impossible because the only information that counts is opinion, whereas judgement corresponds to the public character of factual knowledge. This displacement produces a pseudo-reality where we can imagine that only people like us live here, that is, people who share our own opinions. This is a world that Hannah Arendt and Hans Jonas might characterise as thoughtless.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".