A System Justification Approach to Predicting Collective Action in the Contexts of the 2020 US Presidential Election and a University Name Change
Bibliographic record
Abstract
<p>Collective action refers to actions taken on behalf of a person's group to improve the position of the group; such actions can be normative (peaceful) or non-normative (destructive) (Wright et al., 1990). The social identity model of collective action (SIMCA; van Zomeren et al., 2008) integrates three primary motivators of collective action: perceived injustice, perceived efficacy, and social identity. A modified model, the System Justification Model of Collective Action (Jost et al., 2017), integrates system justification into the SIMCA model. These models, and the collective action literature generally, have primarily focused on normative forms of collective action. Non-normative forms of collective action have been largely ignored. In the current dissertation, I extend the System Justification Model of Collective Action in three ways: 1) by incorporating non-normative collective action in the model; 2) by incorporating system-level, specifically positive (in addition to negative) system-level emotions; and 3) by examining the refined model across different political contexts and issue domains. This "Extended Model" is explored in three studies. In Study 1, a sample of 505 American residents completed an online study in the context of the presidential transfer of power from Donald Trump to Joe Biden, and the Capitol Hill Insurrection. In Study 2, 371 members of the Toronto Metropolitan University community completed the study in the context of attitudes about reconciliation with Indigenous Peoples in Canada, as well as their attitudes on the toppling of the Egerton Ryerson statue, the former namesake of the university. In Study 3, data from 191 participants was extracted from an open-ended question from Study 2 regarding participants’ attitudes toward the toppling of the Ryerson statue. The results replicate existing findings and present partial support for the extended system justification model of collective action.</p>
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".