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Record W2997894838 · doi:10.4324/9781315209999-2

Stereotypes of Ethnic Minorities in the Netherlands

2017· article· en· W2997894838 on OpenAlexaboutno aff
Louk Hagendoorn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupStereotype (UML)FeelingPoliticsQuarter (Canadian coin)Gender studiesPopulationPolitical scienceState (computer science)GeographySociologyDemographySocial psychologyPsychologyLaw

Abstract

fetched live from OpenAlex

In 1648 the Republic of the United Netherlands declared itself an independent state, in a move supported by the European powers gathered in Munster, in order to end a long period of religious wars. The migrant groups currently present in the Netherlands arrived predominantly after the Second World War. The first to arrive were about 180,000 Dutch-Indonesians and 40,000 South-Moluccans from Indonesia in the 1950s. Since the 1980s surveys on the perceptions and feelings of the Dutch about migrants, have generally found that about a quarter to a third of the Dutch population have negative attitudes towards ethnic minorities. Stereotypes were initially considered to be biased rigid and hostile images of one group about other groups. Stereotypes can in many ways extend to political beliefs. Behind the stereotype that migrants take away jobs and houses hides the politically charged image of foreigners invading Dutch territory to grab what belongs to the Dutch.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.365
Teacher spread0.310 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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