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Record W7109069441 · doi:10.17613/qcdz0-xe131

The Psychological Impact of Ostracism and the Silent Treatment and their Application to the Psalms

2025· article· W7109069441 on OpenAlexaff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2025
Typearticle
Language
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsKingswood UniversityMcMaster University
Fundersnot available
KeywordsOstracismFaithAffect (linguistics)Psychological painSocial rejectionSocial discriminationInterpersonal relationship

Abstract

fetched live from OpenAlex

This study applies psychological research on the effects of ostracism and the silent treatment to interpret the psalms of lament—drawing especially on the work of the psychologist Kipling D. Williams. Studies show that receiving the silent treatment affects the same part of the brain that detects physical pain (the anterior cingulate cortex) and there are passages in the psalms that describe the psalmists being shunned by their communities. Yet these negative psychological effects can also happen when the other party is not physically present, such as when one's texts or social media messages are ignored. This insight can be used to understand the psalmists' experiences of unanswered prayer, where God is not physically present. Studies show that the pain of ostracism and the silent treatment can affect readers and audiences second-hand as well. To that end, there is evidence that members of faith communities will be less likely to give generously after hearing Scripture passages where God ignores the psalmist or other biblical writer. Nonetheless, these negative effects are counterbalanced by the positive social aspects of gathering in community and by the fact that even psalms that include ostracism often have positive conclusions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.023
GPT teacher head0.319
Teacher spread0.296 · 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.

Study designNot applicable
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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