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Record W4408312069 · doi:10.54536/jirp.v2i1.3985

The Butterfly Effect and Its Cumulative Role in Promoting Social and Political Change in Sudan

2025· article· en· W4408312069 on OpenAlexaff
Fawzi Ahmed Abdullah Slom

Bibliographic record

VenueJournal of International Relations and Peace · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsPoliticsButterflyButterfly effectPolitical scienceGeographyEconomicsEcologyBiologyManagementLaw

Abstract

fetched live from OpenAlex

The Butterfly Effect, a concept originating from chaos theory, has been increasingly applied to understanding political and social change, particularly in contexts of repression. This study, focusing on Sudan’s recent history, explores how small, localized actions can catalyze broader social and political transformations. The article integrates primary and secondary data collection methods to understand the Butterfly Effect in grassroots resistance. Data Collection is based on primary and secondary data; the primary data is collected from 15 in-depth interviews with key figures from the Salmiya Group and other grassroots movements. The interviews focused on their strategies, challenges, and perspectives on nonviolent resistance and community mobilization. Participants were selected using purposive sampling, targeting individuals directly involved in resistance efforts to provide detailed insights. Secondary Data Analyzed news articles, reports, and social media content documenting the activities of Sudanese resistance movements. Historical accounts of Sudan’s 2019 revolution and the 2021 military coup were included to contextualize the findings and examine the themes and patterns in resistance strategies, including nonviolent resistance, social media activism, and community 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.118

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 head0.018
GPT teacher head0.290
Teacher spread0.272 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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