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Record W2923102738 · doi:10.22215/etd/2015-11032

Seeing Past the 'Post-9/11' Framing: The Long Rise of Anti-Islam Politics in the Netherlands

2015· dissertation· en· W2923102738 on OpenAlexaff
Ottilie Grisdale

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsCarleton University
FundersEuropean Commission
KeywordsIslamFraming (construction)PolityPoliticsMulticulturalismPolitical scienceTerrorismIslamophobiaMainstreamMedia studiesPolitical economyReligious studiesSociologyGender studiesLawHistory

Abstract

fetched live from OpenAlex

In the early 2000s, anti-Islam parties rose to unprecedented prominence in the Netherlands. Within the Dutch polity, the parties' mainstream popularity is widely understood as a product of the post-9/11 climate; defined by "Islamist" terrorist attacks throughout Western Europe, and concurrent political discourses on the "crisis" of multiculturalism. Researchers critical of this interpretation have analysed anti-Islam politics in the Netherlands as a product of the post-9/11 security climate. Yet framing anti-Islam politics as 'post-9/11', underestimates the long-term presence of anti-Islam politics and disguises systemic issues of minority discrimination that have long plagued Dutch society. In order to 'see past' the post-9/11 framing, this paper examines the history of anti-Islam politics within the broader historical context of the Dutch 'multicultural myth' and issues of including newcomers into Dutchness since 1945. The curious trend amongst Dutch politicians to circulate anti-Islam politics through independently released books/films is explored and its significance discussed. A master's thesis is an inherently collaborative endeavour. I would like to thank my supervisor Professor Jennifer Evans for her unwavering support and guidance as this project evolved. Her careful revisions challenged and

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.684
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.312
Teacher spread0.294 · 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 designQualitative
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
Published2015
Admission routes1
Has abstractyes

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