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Contextualizing Shame: The Importance of Culture and Discrimination in the Study of Self-Conscious Emotions

2023· dissertation· en· W6884537426 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2023
Typedissertation
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsShameFeelingCoping (psychology)ImmigrationChina

Abstract

fetched live from OpenAlex

Within Western psychology, shame is often seen as a maladaptive and hard to regulate selfconscious emotion. Yet, there is some emerging evidence that our knowledge of shame, and the emotion itself, are deeply influenced by cultural assumptions. I first start by providing a critical review of the literature on shame, highlighting differing, culturally-informed conceptualizations of shame in the West and in China and Taiwan. This review also highlights the potential role of social threats and discrimination in the social construction of shame for individuals. Study One then qualitatively explores the beliefs about shame and coping strategies used by Pakistani immigrants to Canada, without imposing a Western lens. Study Two tests two common assumptions about shame (i.e., shame and guilt are two distinct, separate emotions; past experiences of discrimination do not need to be systematically included) present in Western psychology in a Canadian (i.e., Western) sample. While shame is an innate emotional experience, findings suggest that shame and guilt may not be two fully distinct and separate emotions, and that past experiences of discrimination are positively associated with feelings of inferiority present in state shame.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.351
Teacher spread0.302 · 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 designQualitative
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

Citations1
Published2023
Admission routes1
Has abstractyes

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