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The Relationship Between Nonword Repetition Skills and Vocabulary Development in Children Under 6 Years of Age
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CPAP-hoidon ja obstruktiivisen uniapnean yhteys parodontiittiin: systemaattinen kirjallisuuskatsaus
No full textRedefining the Front: Borderlands as Strategic Asset
No full textThis chapter challenges traditional security paradigms by reframing borderlands from peripheral liabilities to strategic assets within NATO’s deterrence architecture. Against the backdrop of Russia’s renewed aggression and hybrid warfare campaigns, the analysis demonstrates how borderland regions, particularly those adjacent to Russia, serve as critical laboratories for developing adaptive security responses. It argues that contemporary security challenges transcend conventional inside/outside divides, requiring comprehensive approaches that integrate governmental coordination with societal resilience. Borderlands possess unique capabilities derived from their geographical proximity, experiential knowledge of adversarial behaviour, and adaptive capacity developed through managing persistent uncertainty. These regions contribute to Alliance security through five interconnected dimensions: enhanced intelligence collection and early warning systems, practical knowledge of adversarial patterns, innovative problem-solving approaches, psychological deterrent effects, and tested models of cooperative security. The chapter emphasises that well-prepared borderland communities demonstrate successful resistance to hybrid threats, creating force multiplication effects that enhance overall Alliance credibility. This strategic reframing represents a fundamental shift from protecting vulnerable peripheries to leveraging borderland expertise for collective defence, highlighting the transformation of security thinking in an era of persistent hybrid challenges
Beyond names: how to label gender automatically in CMC data?
No full textLarge-scale data from social media offers numerous benefits for research, but one significant and widely known limitation is the lack of detailed social background information. This gap poses a serious challenge for fields such as sociolinguistics and the study of language variation and change, where demographic and contextual information are crucial. A commonly used approach in computer-mediated communication (CMC) research has been to infer gender from users' names. However, a variety of other methods have emerged in recent years, drawing from advances in machine learning. This presentation reviews the current state of social media data enrichment and
introduces a generalizable method that integrates various types of background information. Enriched data can train machine learning models to label social media user accounts with more accurate gender information
