Pakistani Immigrants' Nuanced Beliefs About Shame and Its Regulation
Bibliographic record
Abstract
Abstract. The present study explored beliefs about shame and coping strategies of Pakistani immigrants to Canada, without imposing Western definitions or theories. Semistructured interviews were conducted with 18 adult Pakistani immigrants to Canada who immigrated within the last 8 years. Grounded theory was used to uncover and illuminate how shame could act as a signal for wrongdoing or emerge as a result of social control and social hierarchies, while in both instances being shaped by and informing complex relational and social contexts. Participants accessed a wide range of positive and negative coping behaviors and prioritized positive coping strategies which included close others and focused on self-improvement. The findings highlight the need for researchers to expand current definitions of shame to render them more inclusive of non-Western worldviews and to honor the diversity in metacognitions or beliefs about shame present in different cultural groups. Future research may also benefit from exploring how shame may be felt as a response to power differentials, and how this may impact individuals' experiences of immigration. It is important for practitioners working with Pakistani immigrants to Canada to honor clients' nuanced and complex cultural and religious knowledge about shame, as Pakistani immigrants' beliefs about shame and their proactive stance toward the regulation of this emotion are likely to be protective. We also encourage therapists to be open to discussing sources of shame (e.g., personal vs. imposed by others) and systemic, structural inequalities which may be important in explaining individuals' emotional experience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".