MétaCan
Menu
← Back to cohort
Record W4398912271 · doi:10.7910/dvn/wtdt8f

Replication Data for: Does Electing Extremist Parties Increase Violence and Intolerance?

2019· dataset· en· W4398912271 on OpenAlexaff
Nicholas Kuipers, Gareth Nellis, Michael Weaver

Bibliographic record

VenueHarvard Dataverse · 2019
Typedataset
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReplication (statistics)Computer securityComputer scienceCriminologyPolitical scienceBusinessPsychologyMedicineVirology

Abstract

fetched live from OpenAlex

We estimate the effect of incumbency by Islamist parties on the incidence of religious violence and intolerance in Indonesia, exploiting discontinuities in the proportional representation system used to allocate seats in district legislative elections---the most local tier of parliamentary government. We find that the presence of additional Islamist (as opposed to secular nationalist) legislators exacerbates religious conflict according to certain measures. There is no evidence that Islamist rule affects average attitudes toward religious minorities among majority-group survey respondents, although it does increase expressions of extreme intolerance. Social emboldening may underlie these effects, as Islamist incumbency appears to boost the perceived acceptability of holding intolerant worldviews. The results shed light on the consequences of having extremist parties gain a share in local power.

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)0.005
metaresearch head score (Gemma)0.026
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.061
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0610.049

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 head0.050
GPT teacher head0.340
Teacher spread0.291 · 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
GenreDataset

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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueHarvard Dataverse→Same topicTerrorism, Counterterrorism, and Political Violence→French-language works237,207→