Bibliographic record
Abstract
Monicah h Gachuki, , Kenya a Medical l Practitioners, , Pharmacists s and d Dentists s Union n (KMPDU), , Kenya a On 18 June, a wave of youth-led protests began to roil Kenya.These protests quickly gained momentum, occurring every Tuesday and Thursday into July.The most significant of these demonstrations took place on 25 June, when protestors occupied parliament.Clashes with police resulted in several deaths.To understand the significance of these events, it is important to consider the broader sociopolitical context in Kenya.The protest movement was not an isolated development but rather reflected deep-seated frustrations among the Kenyan population, particularly the nation's youth.These frustrations stemmed from a range of issues including economic hardship, unemployment, corruption and a perceived disconnect between government priorities and the needs of the people.What made these protests particularly distinct was the composition and approach of the demonstrators.Unlike previous movements, these protests were driven primarily by Gen Z youth.These youths leveraged the use of social media such as X (formerly Twitter) for communication, mobilization and amplification of their demands, showcasing a new era of digital activism in Kenya.This generation of protesters was also notable for being "tribeless", a significant departure from Kenya's historical pattern of ethnic-based politics.This shift indicated a potential political transformation in the country, as the youth rallied around shared ideals rather than tribal affiliations.The protests began as a response to tax hikes, which were largely driven by the need to repay IMF loans.By mid-2020, amid the unfolding Covid-19 pandemic, Kenya's public debt had swelled to nearly 6.3 trillion shillings.This figure represented a daily borrowing rate of about 4.5 billion shillings during the pandemic's early months (Olingo, 2020).The trend continued, with total public debt rising by approximately 17 per cent over the following year, reaching 7.34 trillion shillings by March 2021 (KIPPRA, 2021).The global landscape shifted again in 2022 with the onset of the Russia-Ukraine conflict, further straining Kenya's economy.Under guidance from the IMF, the government shifted the financial burden onto citizens with measures such as doubling the fuel Value Added Tax in July 2023, which led to record-high fuel prices.Additional proposals targeted staple foods such as sugar and maize flour for new taxation, policies that had the most brutal impact on the country's most vulnerable (Kalevera, 2023).Earlier this year, under pressure to conform to IMF conditions, President William Ruto supported a controversial finance bill that included even greater tax hikes.The announcement came as many Kenyans, especially young people, were struggling to make ends meet, with inflation at an all-time high and the cost of living already skyrocketing.The unemployment rate, particularly among youth, had reached alarming levels, leaving many young people feeling neglected and
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".