Rethinking current models in social psychology: A Bayesian framework to understand dramatic social change
Bibliographic record
Abstract
Dramatic social change (DSC) is the new normal, affecting millions of people around the world. However, not all events plunge societies into DSC. According to de la Sablonnière (2017, Front. Psychol., 8, 1), events that have a rapid pace of change, that rupture an entire group's social and normative structures, and that threaten the group's cultural identity will result in DSC. This perspective provokes important unanswered questions: What is the chance that a DSC will occur if an event takes place? And, when will other societal states arise from such events? Addressing these questions is pivotal for a genuine psychology of social change to emerge. The goal of this article was to describe a methodology that attempts to answer these questions via a probabilistic decision tree within a Bayesian framework. According to our analysis, a DSC should occur 6.25% of the time that an event takes place in a stable society (68.75% of the time for incremental social change, 12.5% for inertia, and 12.5% for stability). The Bayesian probabilistic decision tree could be applied to specific event and thus serve as a guide for a programmatic study of social change and ultimately inform policymakers who need to plan and prepare for events that lead to DSC.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".