This article examines the impact of the digital environment on the status and place of language. The focus is on the concept of digital linguistic sovereignty, understood as a nation’s ability to ensure the functional competitiveness of the language in the age of artificial intelligence and big data. In this regard, Kazakhstan presents a unique case study of a country transitioning from traditional language protection to the active construction of digital sovereignty. As a state undergoing active nation-building, the country simultaneously demonstrates one of the highest levels of digitalisation in the post-Soviet space. The implementation of large-scale state programmes (such as “Digital Kazakhstan”) has led to the formation of a new communicative environment – a virtual linguistic landscape – in which the dynamics of interaction between the state language and global languages are assuming qualitatively different forms. The methodological basis of the study is an interdisciplinary approach combining critical discourse analysis of official documents, institutional analysis of technological projects such as the National Corpus of the Kazakh Language, the Coursera platform in Kazakh, the translation of educational materials for higher education institutions, and others, as well as the study of expert discourses in new media. The study’s findings illustrate how the state regulates the process of language planning in the context of increasingly pervasive technological advancement, transitioning from a protective model toward digital modernisation. The creation of mass digital content in academic and technical fields has been demonstrated to contribute to the growing intellectual recognition of the Kazakh language. The authors of the study argue for the need to transition from formal status planning to an inclusive model of digital citizenship in order to achieve “linguistic justice.” It has been established that language digitalization is becoming a tool of “soft power,” transforming civic identity and expanding access to digital capital. The practical significance of this study lies in the potential application of the proposed analytical framework to the study of language processes in other multilingual societies undergoing digital transformation.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al.· IEEE International Conferenc...· 110 citations· ⚡7
The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduSep 14, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.