Using the SIRDE model of social change to examine the vote of Scottish teenagers in the 2014 independence referendum
Bibliographic record
Abstract
Five hundred and seventy-three Scottish high school students were surveyed in the 2 months following the 2014 referendum on Scotland's independence. We used the Social Identity, Relative Deprivation, collective Efficacy (SIRDE) model of social change to examine the social psychological factors that should have influenced the voting choices of these teenagers. Structural equation modelling indicated that the SIRDE model fit the data and largely supported four sets of hypotheses derived from the model. Specifically, (1) those with a stronger Scottish identity, (2) those who felt frustrated and angry that Scottish people are discriminated against in British society, and (3) those who believed that Scottish people are not able to improve their relatively poor social conditions within the United Kingdom (a lack of collective efficacy) were more likely to hold separatist beliefs. Further, the relationships between identity, relative deprivation, and collective efficacy, on the one hand, and voting for Scotland's independence, on the other, were fully mediated by separatist social change beliefs. Consistent with the specificity of the model, neither political engagement nor personal relative deprivation were associated with voting choice, whereas the latter was associated with lower life satisfaction. The implications and limitations of these findings are discussed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".