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Record W2341296184 · doi:10.1057/9781137339317_5

Civic and Economic Nationalism: The Scottish Turn to Immigration

2015· book-chapter· en· W2341296184 on OpenAlexaboutno aff
Fiona Barker

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2015
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationReferendumPoliticsXenophobiaPolitical scienceDevolution (biology)NationalismBrexitPolitical economyPopulationSociologyDevelopment economicsEuropean unionLawDemography

Abstract

fetched live from OpenAlex

Several months before the 2014 independence referendum, Scottish Education Secretary, Mike Russell, described UK immigration policy as ‘driven by UKIP and by a nasty xenophobia which … revolts me’ (BBC News 2014). His comments exemplified a common practice of Scottish political leaders of distinguishing what they argue to be an open and inclusionary ‘Scottish approach’ to immigrants and diversity from increasing restrictionism in the United Kingdom. Much later than their counterparts in Quebec and Flanders, Scottish political leaders have recently begun to grapple with the question of how to respond to growing ethnic, linguistic and religious diversity of the population ‘north of the border’. They have also considered whether, and how, the politics of immigration relates to the politics of multinationalism in the multilevel state. Despite the long history of immigration in the United Kingdom, immigration and ‘visible’ diversity are more recent phenomena in Scotland. Moreover, under devolution Scotland has occupied a relatively weak position in terms of both its general constitutional power and its specific capacity to govern immigration, migrant integration and diversity. These factors, taken alongside the non-linguistic basis of Scottish substate nationalism, make Scotland a distinctive setting in the analysis of how substate political leaders respond to questions of immigration and migrant integration. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.091
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.013
Scholarly communication0.0080.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.029
GPT teacher head0.277
Teacher spread0.248 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2015
Admission routes1
Has abstractyes

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