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Record W4404725596 · doi:10.1177/13634615241299556

The fragility of truth: Social epistemology in a time of polarization and pandemic

2024· editorial· en· W4404725596 on OpenAlexaffabout
Laurence J. Kirmayer

Bibliographic record

VenueTranscultural Psychiatry · 2024
Typeeditorial
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsMcGill University
Fundersnot available
KeywordsSociologyParanoiaPoliticsEpistemologyMisinformationPolitical sciencePsychologyLawPsychiatry

Abstract

fetched live from OpenAlex

presenting selected papers from the 2022 McGill Advanced Study Institute in Cultural Psychiatry on "The Fragility of Truth: Social Epistemology in a Time of Polarization and Pandemic." The COVID-19 pandemic, political polarization, and the climate crisis have revealed that large segments of the population do not trust the best available knowledge and expertise in making vital decisions regarding their health, the governance of society, and the fate of the planet. What guides information-seeking, trust in authority, and decision-making in each of these domains? Articles in this issue include case studies of the dynamics of misinformation and disinformation; the adaptive functions and pathologies of belief, paranoia, and conspiracy theories; and strategies to foster and maintain diverse knowledge ecologies. Efforts to understand the psychological dynamics of pathological conviction have something useful to teach us about our vulnerability as knowers and believers. However, this individual psychological account needs to be supplemented with a broader social view of the politics of knowledge and epistemic authority that can inform efforts to create healthy information ecologies and strengthen the civic institutions and practices needed to provide well-informed pictures of the world as a basis for deliberative democracy, pluralism, and co-existence.

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.

How this classification was reachedexpand

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.469
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.013
GPT teacher head0.316
Teacher spread0.303 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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