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Populism, Fake News, and the Flight From Democracy

2020· book-chapter· en· W3006679893 on OpenAlex
Greg M. Nielsen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in media, entertainment and the arts (AMEA) book series · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsConcordia University
Fundersnot available
KeywordsPopulismDemocracyFake newsOligarchyReactionaryPolitical sciencePoliticsPresidential systemOpposition (politics)JournalismPower (physics)Political economyMedia studiesSociologyLaw

Abstract

fetched live from OpenAlex

Fake news and populist movements that appear to hold the fate of democracy hostage are urgent concerns around the world. The flight from liberal democracy toward oligarchy has spread out from the unexpected results of the 2016 American presidential elections bringing in a wave of reactionary populism and the beginning of a left populist counter movement. The phenomenon of fake news is often explained in terms of opposition public relations strategies and geopolitics that shift audiences toward a regime of post-truth where emotion is said to triumphs over reason, computational propaganda over common sense, or sheer power over knowledge. In this chapter, the authors propose something different in order to theorize the imaginary audience(s) and conditions of reception for fake news treated as both a symptom (often of injury) and a cause (at times a danger to democracy). This leads them to evaluate the role it plays in defining what the fields of journalism, politics, and social science are becoming and what it means for democracy to come.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.247
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it