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Record W4411613247 · doi:10.1017/psrm.2025.10022

The value of dignity appeals: evidence from a social media experiment

2025· article· en· W4411613247 on OpenAlexfundno aff
Paige Bollen, Will Kymlicka, Evan S. Lieberman, Blair Read

Bibliographic record

VenuePolitical Science Research and Methods · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsnot available
FundersCanadian Institute for Advanced Research
KeywordsDignityValue (mathematics)Social mediaSociologyEconomicsSocial psychologyPsychologyPositive economicsPolitical scienceMathematicsStatisticsLaw

Abstract

fetched live from OpenAlex

Abstract In recent decades, activists and leaders of government and nongovernment organizations have increasingly and explicitly called for greater attention to human dignity in their efforts to promote pro-social relations. In this study, we investigate whether appeals to this core human value actually influence how individuals act with regard to those who might be otherwise ignored or neglected. Using the digital advertising platform on Facebook, we randomly assign ads to over 90,000 adult American users to estimate the effects of dignity appeals on their likelihood of engaging with content concerning people facing homelessness or incarceration. Consistent with preregistered hypotheses and specifications, we find that adding dignity appeals increases the likelihood of positive reactions to such ads, but only when the vulnerable are considered less “blameworthy” for their situation.

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)0.011
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.061
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.361
GPT teacher head0.634
Teacher spread0.274 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations0
Published2025
Admission routes1
Has abstractyes

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