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Record W4389761840 · doi:10.1080/00083968.2023.2268751

Controlling consent, dealing with dissent, and planting misinformation: how the Museveni regime stifled Bobi Wine's youth movement in Uganda

2023· article· en· W4389761840 on OpenAlexafffundvenue
Gerald Bareebe

Bibliographic record

VenueCanadian Journal of African Studies / Revue canadienne des études africaines · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Contemporary Political Dynamics
Canadian institutionsYork University
FundersYork University
KeywordsDissentMisinformationPolitical sciencePolitical economyLawEconomicsPolitics

Abstract

fetched live from OpenAlex

After Uganda embraced electoral democracy in 1996, a trend emerged where Ugandans between 18 and 35 do not turn out to vote in large numbers. Although the country is astonishingly young, Ugandan youth had long shown little interest in politics and were less likely to run for public positions compared to older Ugandans. However, the meteoric rise of Bobi Wine has inspired many young Ugandans to engage in local and national politics as first-time voters, party members, cash contributors, protesters and foot soldiers. This article explains how the rise of Robert Kyagulanyi, also known as Bobi Wine, a popular singer turned opponent of President Museveni, has inspired an upsurge in youth political participation in Uganda. The article posits that, to contain a growing youth-led movement, the Museveni regime largely relied on coercion, which has thus far proved counterproductive. The more coercion is employed the more it aggravates the backlash from and determination of young Ugandans to mobilise against Museveni’s rule. The response from the regime has been a mixture of repression, disinformation and disenfranchisement of young voters.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.062
GPT teacher head0.218
Teacher spread0.156 · 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

Citations8
Published2023
Admission routes3
Has abstractyes

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