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Record W2498543166 · doi:10.22329/il.v36i2.4662

Enhancing Rationality: Heuristics, Biases, and The Critical Thinking Project

2016· article· en· W2498543166 on OpenAlex

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueInformal Logic · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicInnovations in Educational Methods
Canadian institutionsCapilano University
Fundersnot available
KeywordsIrrationalityHeuristicsRationalityIrrational numberCognitive biasEcological rationalityRational planning modelCritical thinkingDecision field theoryPsychologyManagement scienceFocus (optics)CognitionBounded rationalityBehavioral economicsEpistemologySocial psychologyEconomicsBusiness decision mappingComputer scienceDecision analysisDecision engineeringMicroeconomicsMathematical economicsManagementMathematics educationMathematics

Abstract

fetched live from OpenAlex

Abstract: This paper develops four related claims: 1. Critical thinking should focus more on decision making, 2. the heuristics and bias literature developed by cognitive psychologists and behavioral economists provides many insights into human irrationality which can be useful in critical thinking instruction, 3. unfortunately the “rational choice” norms used by behavioral economists to identify “biased” decision making narrowly equate rational decision making with the efficient pursuit of individual satisfaction; deviations from these norms should not be treated as an irrational bias, 4. a richer, procedural theory of rational decision making should be the basis for critical thinking instruction in decision making.

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)0.004
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.850
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.102
GPT teacher head0.443
Teacher spread0.342 · 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