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Record W4375860485 · doi:10.1037/xap0000476

Morality in minimally deceptive environments.

2023· article· en· W4375860485 on OpenAlex
Panagiotis Mitkidis, Sonja Perkovic, Aaron Nichols, Philipp Gerlach, Dan Ariely

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

VenueJournal of Experimental Psychology Applied · 2023
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsMoralityPsychologyComputer securityComputer scienceEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Psychologists, economists, and philosophers have long argued that in environments where deception is normative, moral behavior is harmed. In this article, we show that individuals making decisions within minimally deceptive environments do not behave more dishonestly than in nondeceptive environments. We demonstrate the latter using an example of experimental deception within established institutions, such as laboratories and institutional review boards. We experimentally manipulated whether participants received information about their deception. Across three well-powered studies, we empirically demonstrate that minimally deceptive environments do not affect downstream dishonest behavior. Only when participants were in a minimally deceptive environment and aware of being observed, their dishonest behavior decreased. Our results show that the relationship between deception and dishonesty might be more complicated than previous interpretations have suggested and expand the understanding of how deception might affect (im)moral behavior. We discuss possible limitations and future directions as well as the applied nature of these findings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.881

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.132
GPT teacher head0.364
Teacher spread0.232 · 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