Mobilizing IDEAS in the Scottish Referendum: Predicting voting intention and well‐being with the Identity‐Deprivation‐Efficacy‐Action‐Subjective well‐being model
Bibliographic record
Abstract
In the month approaching the 2014 Scottish Independence referendum, we tested the Identity-Deprivation-Efficacy-Action-Subjective Well-Being model using an electorally representative survey of Scottish adults (N = 1,156) to predict voting for independence and subjective well-being. Based on social identity theory, we hypothesized for voting intention that the effects of collective relative deprivation, group identification, and collective efficacy, but not personal relative deprivation (PRD), should be fully mediated by social change ideology. Well-being was predicted to be associated with PRD (negatively) and group identification (positively and, indirectly, negatively). Unaffected by demographic variables and differences in political interest, nested structural equation model tests supported the model, accounting for 82% of the variance in voting intention and 31% of the variance in subjective well-being. However, effects involving efficacy depended on its temporal framing. We consider different ways that social identification can simultaneously enhance and diminish well-being and we discuss ramifications of the model for collective mobilization and separatist nationalism. Findings also suggest new directions for research on social identity, collective efficacy, and collective action.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".