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Record W1859258661 · doi:10.22329/il.v20i3.2283

Stanovich's Who Is Rational? Studies of Individual Differences in Reasoning

2000· article· en· W1859258661 on OpenAlexaffvenue
David Hitchcock

Bibliographic record

VenueInformal Logic · 2000
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEpistemologyPsychologySocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

This is an important book.It addresses the question: Are human beings systematically irrational?They would be so if they were "hard-wired" to reason badly on certain types of tasks.Even if they could discover on reflection that the reasoning was bad, the unreflective tendency to reason badly would be a systematic irrationality.According to Stanovich, psychologists have shown that "people assess probabilities incorrectly, they display confirmation bias, they test hypotheses inefficiently, they violate the axioms of utility theory, they do not properly calibrate degrees of belief, they overproject their own opinions onto others, they allow prior knowledge to become implicated in deductive reasoning, they systematically underweight information about nonoccurrence when evaluating covariation, and they display numerous other information-processing biases."(1-2) Such cognitive psychologists as Nisbett and Ross (1980) and Kahneman, Slovic and Tversky (1982) interpret this apparently dismal typical performance as evidence of hard-wired "heuristics and biases" (whose presence can be given an evolutionary explanation) which are sometimes irrational.Critics have proposed four alternative explanations.(1) Are the deficiencies just unsystematic performance errors of basically competent subjects due to such temporary psychological malfunctions as inattention or memory lapses?Stanovich and West (1998a) administered to the same subjects four types of reasoning tests: syllogistic reasoning, selection, statistical reasoning, argument evaluation.They assumed that, ifmistakes were random performance errors, there would no significant correlation between scores on the different types of tests.In fact, they found modest but statistically very significant correlations (at the .001level) between all pairs of scores except those on statistical reasoning and argument evaluation.Hence, they concluded, not all mistakes on such reasoning tasks are random performance errors.

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)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.011
Scholarly communication0.0050.007
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.255
GPT teacher head0.421
Teacher spread0.166 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2000
Admission routes2
Has abstractyes

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