Kenya’s Protests Herald a New Age of Anti-Austerity Youth Politics
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,010 | 0,004 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».