From #Hashtags to the Streets: The Rising Tide of African Protests and the Quest for Leadership Accountability
Bibliographic record
Abstract
This paper explores the recent wave of protest movements across Africa, notably the Nigeria's #EndBadGovernance, Kenya's #KOT movement, and Uganda's #UgandaParliamentExhibition. These movements have been interpreted as indicators of a burgeoning collective consciousness among citizens (Honwana, 2014; Chiamogu et al., 2021). However, this emerging awareness prompts critical inquiries into whether these protests can truly drive substantive change, especially considering Africa's long history of similar movements. The paper delves into whether these contemporary protests can achieve what previous ones could not, particularly in terms of altering the entrenched attitudes of African ruling elites known for poor governance. It is reasonable to question whether what is perceived as a conscious awakening is actually driven by the sheer number of public participation, individuals tweeting, and blogging about these issues, thereby creating a theatricalized media presence that fuels rolling news coverage. Suffice it to say that while each perception and action can provoke significant responses from protest movements, these responses typically occur instinctively and rapidly, without conscious awareness. While these protests are praiseworthy for their impact, the paper argues that they represent instinctual, rapid responses rather than deliberate, informed actions. Consequently, while these movements are vital in highlighting grievances, they are ultimately insufficient to enacting meaningful, systemic change of leadership in Africa, because they lack the strategic depth and sustained effort necessary to address the continent's deep-rooted issues in the absence of a fundamental revolution of thought.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".