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Record W2313697462 · doi:10.1371/journal.pone.0152798

The Immoral Landscape? Scientists Are Associated with Violations of Morality

2016· article· en· W2313697462 on OpenAlexaff
Bastiaan T. Rutjens, Steven J. Heine

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of British Columbia
FundersAXA Research Fund
KeywordsImmoralityMoralityAtheismSocial psychologyContext (archaeology)LoyaltyPsychologyAssociation (psychology)Environmental ethicsEpistemologyPolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

Do people think that scientists are bad people? Although surveys find that science is a highly respected profession, a growing discourse has emerged regarding how science is often judged negatively. We report ten studies (N = 2328) that investigated morality judgments of scientists and compared those with judgments of various control groups, including atheists. A persistent intuitive association between scientists and disturbing immoral conduct emerged for violations of the binding moral foundations, particularly when this pertained to violations of purity. However, there was no association in the context of the individualizing moral foundations related to fairness and care. Other evidence found that scientists were perceived as similar to others in their concerns with the individualizing moral foundations of fairness and care, yet as departing for all of the binding foundations of loyalty, authority, and purity. Furthermore, participants stereotyped scientists particularly as robot-like and lacking emotions, as well as valuing knowledge over morality and being potentially dangerous. The observed intuitive immorality associations are partially due to these explicit stereotypes but do not correlate with any perceived atheism. We conclude that scientists are perceived not as inherently immoral, but as capable of immoral conduct.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.154

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)0.0000.000

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 head0.160
GPT teacher head0.259
Teacher spread0.099 · 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 teacher head, 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

Citations90
Published2016
Admission routes1
Has abstractyes

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