Toward a Psychology of Social Change: A Typology of Social Change
Bibliographic record
Abstract
Millions of people worldwide are affected by dramatic social change (DSC). While sociological theory aims to understand its precipitants, the psychological consequences remain poorly understood. A large-scale literature review pointed to the desperate need for a typology of social change that might guide theory and research toward a better understanding of the psychology of social change. Over 5,000 abstracts from peer-reviewed articles were assessed from sociological and psychological publications. Based on stringent inclusion criteria, a final 325 articles were used to construct a novel, multi-level typology designed to conceptualize and categorize social change in terms of its psychological threat to psychological well-being. The typology of social change includes four social contexts: Stability, Inertia, Incremental Social Change and, finally, DSC. Four characteristics of DSC were further identified: the pace of social change, rupture to the social structure, rupture to the normative structure, and the level of threat to one's cultural identity. A theoretical model that links the characteristics of social change together and with the social contexts is also suggested. The typology of social change as well as our theoretical proposition may serve as a foundation for future investigations and increase our understanding of the psychologically adaptive mechanisms used in the wake of DSC.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".