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Record W2585972597 · doi:10.1111/bjso.12186

Using the SIRDE model of social change to examine the vote of Scottish teenagers in the 2014 independence referendum

2017· article· en· W2585972597 on OpenAlexaff
Peter R. Grant, Mark Bennett, Dominic Abrams

Bibliographic record

VenueBritish Journal of Social Psychology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsReferendumRelative deprivationSocial psychologyVotingPsychologyIndependence (probability theory)Social identity theoryIdentity (music)Collective identityPoliticsSocial groupPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.224
GPT teacher head0.442
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2017
Admission routes1
Has abstractyes

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