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Record W4404014741 · doi:10.1111/nana.13062

How attitudes towards independence reshaped Scottish elections: A longitudinal analysis (1999–2021)

2024· article· en· W4404014741 on OpenAlexafffund
Jean‐François Daoust, Thomas Gareau‐Paquette

Bibliographic record

VenueNations and Nationalism · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsUniversité de Sherbrooke
FundersUniversité de MontréalAmerican Political Science Association
KeywordsIndependence (probability theory)Political sciencePublic administrationPolitical economySociologyStatisticsMathematics

Abstract

fetched live from OpenAlex

Abstract British and Scottish politics are undergoing a period of tumultuous change, leading to what some scholars have labelled ‘electoral shocks’, which have altered the relative importance of the main issues explaining citizens' political behaviour. The conventional wisdom now posits that attitudes towards Scottish independence play an increasing role in structuring citizens' vote choice in Scotland. However, a longitudinal assessment of this claim is lacking. In this research, we seek to fill this gap by making use of data from 1999 to 2021, which covers every Scottish election since devolution, and providing the first‐ever systematic assessment of the strength of attitudes towards Scottish independence in explaining electoral behaviour over time. Our findings reveal a substantial increase in the importance of attitudes towards independence, which we can quantify throughout the 1999–2021 period. We conclude by discussing the implications of this for British and Scottish politics.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.478

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.032
GPT teacher head0.345
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2024
Admission routes2
Has abstractyes

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