When the “We” Impacts How “I” Feel About Myself
Bibliographic record
Abstract
Dramatic social change leads to profound societal transformations in many countries around the world. The two recent revolutions in March 2005 and April 2010, and the ethnic conflict in June 2010 in Kyrgyzstan are vivid examples. The present research aims to understand people’s reactions to dramatic social change in terms of personal well-being. To further understand how people react psychologically to dramatic social change, the theoretical framework of our research is based on a dominant theory in social psychology: Collective relative deprivation theory. In the past, researchers have argued that collective relative deprivation is logically associated with collective outcomes, and thus is not likely to impact personal well-being (e.g., Walker & Mann, 1987 ). Others, however, have argued that feelings of collective relative deprivation do impact personal well-being (e.g., Zagefka & Brown, 2005 ). We postulate that these inconsistent results arise because past research has failed to consider multiple points of comparison over time to assess collective relative deprivation. Specifically, we theorize that multiple points of collective relative deprivation need to be taken into account, and in so doing, collective relative deprivation will, indeed, be related to personal well-being. We also explore the entire trajectory of collective relative deprivation (which represents how an individual perceives the evolution of his/her group’s history across time) to predict personal well-being. In the present study, we tested these theoretical propositions in the context of dramatic social change in Kyrgyzstan. Regressions, group-based trajectory modeling, and MANOVA confirm our hypotheses.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".