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Record W2891529846

When Do Religious Minorities' Grievances Lead to Peaceful or Violent Protest? Evidence from Canada’s Jewish and Muslim Communities

2018· preprint· en· W2891529846 on OpenAlexaboutno aff
Christopher Huber, Matthias Basedau

Bibliographic record

VenueEconstor (Econstor) · 2018
Typepreprint
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
Fundersnot available
KeywordsDissentJudaismPoliticsPolitical sciencePropositionRelative deprivationCriminologyPolitical economySociologyLawGeography
DOInot available

Abstract

fetched live from OpenAlex

Previous research has shown that minority grievances can contribute significantly to violent conflict. However, it appears that grievances do not inevitably induce religious and other minorities to engage in protest or rebellion. Moreover, relative deprivation may explain conflict but not necessarily violent conflict. Contributing to research on these questions, this paper explores the conditions under which the grievances of religious minorities lead to non-violent or violent protest. Using a motive-opportunity framework, we assume that members of religious minorities who feel discriminated against must be willing and able to engage in peaceful and violent forms of protest - and that certain conditions are required for grievances to result in peaceful or violent dissent. We test this proposition by comparing the Jewish and Muslim communities in Canada. Our findings indicate that relative economic and political deprivation may create concrete grievances that in combination with origin-based value incompatibilities can explain differences in behaviour in reaction to these grievances.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.297
Teacher spread0.258 · 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.

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

Citations10
Published2018
Admission routes1
Has abstractyes

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