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Verbal Emotional Disclosure of Moral Injury in Holodomor Survivors

2020· article· en· W3110723132 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePSYCHOLINGUISTICS · 2020
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsMoral injuryPsychologySocial psychology

Abstract

fetched live from OpenAlex

Objectives. The purpose of the current research is to define and operationalize moral injury based on moral standards, moral judgements, moral reasoning, moral emotions, moral behaviour, and moral consequences; to explore verbal emotional disclosure of moral injury in Holodomor survivors’ narratives. Materials & Methods. The study applies traumatic narratives of 42 survivors of the Holodomor of 1932–1933 in Ukraine. Main themes aligned with morality structure were captured, using software tool NVivo.12. The study uses LIWC2015 to search for psychological meaningful categories, notably anxiety, anger, sadness, and insights (deep comprehension). The research uses the cross-sectional design utilizing the independent variables of anxiety, anger, insights and dependent variable of moral emotions represented in narratives for multiple linear regression analysis and correlations (2-tailed Pearson r) between components of morality, anxiety, sadness and insights, SPSS. 26. Results. There is a high frequency of moral judgements and a low frequency of moral emotions and moral consequences in the narratives. A significant positive correlation was found between moral standards and other components of morality, in particular moral judgements, moral reasoning, moral consequences, anxiety, sadness and insight. There was a significant positive correlation between moral emotions and anger, and insight. Anxiety, insights and anger taken together are significant predictors of moral emotions, however, only anger is a robust significant independent predictor of moral emotions. Conclusions. Verbal emotional disclosure of traumatic experience relates to expressing righteous anger, contempt, disgust, decreased empathy, and embarrassment, which substitute other moral emotions, notably shame and guilt. The study contributes to our understanding of anxiety, anger, insights (deep comprehension) taken together as robust predictors of moral emotions. Finally, we captured that there are difficulties in verbal emotional disclosure of experience and moral consequences of the Holodomor, since Holodomor survivors predominantly focus on moral judgements and moral standards.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score1.000

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)0.0010.000

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 head0.070
GPT teacher head0.359
Teacher spread0.289 · 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