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Record W3122703912 · doi:10.1007/s10683-016-9479-y

Clever enough to tell the truth

2016· article· en· W3122703912 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.

Bibliographic record

VenueExperimental Economics · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsHonestyThursdayGeneralizability theoryPsychologyConsistency (knowledge bases)Social psychologyCognitionSample (material)ReplicateComputer scienceTheologyDevelopmental psychologyPhilosophyStatisticsMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract We conduct a field experiment on 427 Israeli soldiers who each rolled a six-sided die in private and reported the outcome. For every point reported, the soldier received an additional half-hour early release from the army base on Thursday afternoon. We find that the higher a soldier’s military entrance score, the more honest he is on average. We replicate this finding on a sample of 156 civilians paid in cash for their die reports. Furthermore, the civilian experiments reveal that two measures of cognitive ability predict honesty, whereas general self-report honesty questions and a consistency check among them are of no value. We provide a rationale for the relationship between cognitive ability and honesty and discuss its generalizability.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.040
GPT teacher head0.328
Teacher spread0.288 · 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