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Record W1966136681 · doi:10.1177/0963662511403039

Risk assessment as rhetorical practice: The ironic mathematics behind terrorism, banking, and public policy

2011· article· en· W1966136681 on OpenAlexaff
Robert Danisch

Bibliographic record

VenuePublic Understanding of Science · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsConcordia University
Fundersnot available
KeywordsTerrorismRhetorical questionContingency planHomeland securityContingencyPoliticsRisk managementRisk assessmentRecessionPolitical scienceActuarial scienceEconomicsSociologyLawEpistemologyFinanceManagement

Abstract

fetched live from OpenAlex

The twin problems of possible terrorist attacks and a global economic recession have been, and continue to be, critical components of contemporary political culture. At the center of both problems is the assessment of future risk. To calculate the probability that a loan will default or to estimate the likelihood of an act of bioterrorism crippling an American city is to engage in the quantitative science of risk assessment. The process of risk assessment is an attempt to rationalize the uncertainty and contingency of the future. In this essay, I read risk assessments made by the Department of Homeland Security and by major banks during the recent financial collapse as examples of rhetorical practice. As such, I show the rhetorical form and function of risk assessments in order to determine the effect that they have on contemporary political culture.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0090.065
Scholarly communication0.0150.018
Open science0.0010.004
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.171
GPT teacher head0.399
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations7
Published2011
Admission routes1
Has abstractyes

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