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Record W2990062806 · doi:10.1037/xge0000723

Language framing shapes dehumanization of groups: A successful replication and extension of Cooley et al. (2017).

2019· article· en· W2990062806 on OpenAlexaff
Gordon Hodson, Claire Doucher

Bibliographic record

VenueJournal of Experimental Psychology General · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDiscourse Analysis and Cultural Communication
Canadian institutionsDalhousie UniversityBrock University
Fundersnot available
KeywordsDehumanizationPsychologyFraming (construction)Extension (predicate logic)Social psychologyReplication (statistics)Cognitive psychologyLinguisticsCognitive scienceComputer sciencePhilosophyPolitical scienceStatisticsMathematicsLaw

Abstract

fetched live from OpenAlex

In this journal, Cooley et al. (2017, Study 3) showed that presenting a social target as a group, as opposed to a group composite (i.e., people in a group) or as an individual, lowered perceptions that the target had a sense of mind (perceived capacity for experience and agency), both of which subsequently predicted lower sympathy for the target. In a direct replication but using double the sample size and preregistered hypotheses and methods, we found results strikingly supportive of the target article. We also expanded their findings, showing the effects (particularly of perceived experience) on willingness to help a target in need. The implications for using short communications to promote social change, particularly in a viral social media world, are discussed. (PsycInfo Database Record (c) 2020 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.

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)0.010
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.033
GPT teacher head0.421
Teacher spread0.388 · 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.

Study designObservational
DomainReproducibility
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

Citations8
Published2019
Admission routes1
Has abstractyes

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