MétaCan
Menu
Back to cohort
Record W6929695568 · doi:10.5167/uzh-170661

Facets of nativism: a heuristic exploration

2019· article· en· W6929695568 on OpenAlexaboutno aff

Bibliographic record

VenueZurich Open Repository and Archive (University of Zurich) · 2019
Typearticle
Languageen
FieldMathematics
TopicStatistical Distribution Estimation and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPopulismPsychological nativismGovernment (linguistics)NationalismWelfareIdentity (music)Heuristic

Abstract

fetched live from OpenAlex

Contemporary radical right-wing populism is an ideational compound of anti-elite populism and nativism, the latter encapsulated in the notion that 'the own people' should come first. Like populism, nativism has proven to be a rather elusive concept, particularly when it comes to its relationship to related concepts, such as patriotism, nationalism and particularly racism. Originally developed to analyse anti-immigrant sentiments in the United States and Canada, the term 'nativism' has recently been increasingly used to understand the success of the radical populist right in Europe and elsewhere. In this article, Betz present three facets of nativism: economic nativism, centred on the notion that jobs should be reserved for native citizens; welfare chauvinism, based on the notion that native citizens should be accorded absolute priority when it comes to social benefits; and symbolic nativism, advancing the notion that government should do everything to defend the cultural identity of a given national society. Whereas, in the past, economic considerations, including concerns about the viability of the welfare state, were central to anti-immigrant sentiment, in recent years, symbolic nativism, grounded in a defence of national cultural identity, is central to the success of radical right-wing populist mobilization.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.693
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.048
GPT teacher head0.291
Teacher spread0.243 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueZurich Open Repository and Archive (University of Zurich)Same topicStatistical Distribution Estimation and ApplicationsFrench-language works237,207