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Hybrid Work, Hybrid Identities: Identity Regulation among Gig Workers in Sub-Saharan Africa
2025-12-07 - Barbosa, Isabel Cristina Pereira; Real, Elizabeth
The expansion of the gig economy in sub-Saharan Africa is transforming local labour markets and how workers construct their identities. Despite the growing prevalence of digital labour platforms, little is known about how they regulate workers’ identities in non-Western contexts or how local cultural and societal factors mediate these processes. This article addresses these gaps by investigating identity regulation among gig workers in Nigeria, Kenya, and South Africa. Employing a qualitative, multisite case study design, we draw on semi-structured interviews, digital ethnography, and document analysis to explore three core questions: How do gig platforms in sub-Saharan Africa regulate the identities of their workers? What local cultural and societal factors influence these processes? How do gig workers respond and negotiate identity regulation? Findings reveal that platforms shape worker identities through branding, algorithmic management, and customer feedback. However, these efforts are refracted through powerful local norms, family expectations, and informal worker networks. Gig workers employ strategies from adaptation and compliance to resistance and hybridization to navigate tensions between platform-imposed and locally valued identities. This study extends identity regulation frameworks to digital, non-Western contexts, highlighting hybrid work identities. Results offer insights for platform designers, worker organizations, and policymakers seeking more inclusive, context-sensitive digital labour.
Generalization analysis of ANN-Based routing protocols for diverse vehicular environments in VANETs
2025-12-01 - Mande, Spandana; Begum, Shaik Salma Asiya; Durga, Putta; Mohanty, Sachi Nandan; Moreira, Fernando
The rapid advancement of intelligent transportation systems depends on effective and secure routing within vehicular communication networks under diverse driving conditions. This research investigates the efficacy of neural network-based routing protocols in enabling reliable and secure data transmission under diverse traffic conditions. Prior research has shown that neural networks can alleviate security threats, such as malicious node attacks; however, there has been inadequate exploration of their adaptability in urban, rural, and highway environments. This research examines routing performance in simulated traffic environments and actual mobility data to address this gap. The methodology utilizes robustness testing, transfer learning, and cross-domain validation to evaluate the sensitivity of routing models to variations in vehicle density, mobility patterns, and road configurations. The findings indicate that the neural network-based approach outperforms a conventional routing protocol across various contexts. In urban areas, the delivery rate increased from 78 % to 85 %, while in rural regions, it rose from 65 % to 77 %. We reduced the end-to-end delay by approximately 7 to 12 milliseconds in all instances. Relative to the baseline, throughput increased by approximately 10 to 15 percent, while energy efficiency improved by 5 to 8 percent. The proposed method enhanced system resilience against attacks, successfully thwarting over 90 % of malicious disruptions, in contrast to the 73 to 79 % efficacy of the previous protocol. This study presents a framework for designing adaptive and scalable routing systems that maintain consistent performance across diverse vehicular conditions. The findings enhance the safety and efficacy of intelligent transportation systems.
A inversao do contencioso no codigo de processo civil portugues
2024-12-01 - Barbosa, Ana Raquel
Artigo publicado na Revista Eletrônica Norte Mineira de Direito Erga Omnes.