Global Journal of Social Sciences Studies

A reinforcement learning model for the knowledge graph of imperial guangdong maritime customs archival translation

Chen Lilan — Mon, 20 Jul 2026 00:00:00 -0500

The Guangdong Provincial Archives stores a large volume of valuable historical original Customs archives which are enormous in number and volume, formal in format, various in types and genres, rich in content, providing precious value for such studies as those of Imperial Chinese and foreign exchanges. It has always been the focus of research in the academic world at home and abroad. This paper, characterized by the inputting and outputting relationship among the entities (proper names of people, places, titles, weights and measures) that are most closely related to the translation of Imperial Guangdong Maritime Customs archives, constructs the knowledge graph of Imperial Guangdong Maritime Customs archival translation. In this paper, representative subgraphs being calculated based on the given node sets, a novel archival translation knowledge graph method is proposed, and the spatial components are integrated into the reinforcement learning framework to help understand the knowledge graph of archival translation. Through establishing the Imperial Guangdong Maritime Customs archival translation theory model, the internal structure and external information of archives are analyzed to obtain a more comprehensive and holistic view of the archival translation tasks. At last, the model proposed in this paper is proved to be reasonable by evaluating the data sets of Imperial Guangdong Maritime Customs archives.

Hedging as prepositional focusing: A learnable-theoretic account of the persuasive sweet spot

Luca Magni — Fri, 07 Aug 2026 00:00:00 -0500

This paper develops an account of why hedging, the linguistic marking of uncertainty, can in specific configurations increase rather than diminish persuasive impact, extending recent evidence that hedges combining high-likelihood language with personal perspective occupy a communicative sweet spot. Using the conceptual apparatus of Learnable Theory, the Learnable Enhanced Bhaskarian Ontology (LEBO), prepositional focusing and defocusing, the Complementary Semantic Distance Index (CSDI), the umbra cone construct, and Differenza Inversa (Inverse Difference), the paper reframes hedging not as a graded modulation of propositional certainty but as an act of prepositional attachment occurring at the Learnable stratum, in which a speaker either anchors a claim to a locatable subject, the self, or leaves it ownerless. The sweet-spot register is shown to carry a predictable CSDI signature, Self-Actualization-Positive, which produces an umbra cone effect shadowing Safety-domain content within the same message, so that maximal persuasiveness and maximal disclosure pull in opposite directions. Differenza Inversa, originally an intrapsychic operator for reinterpreting autobiographical memory, is extended here, as a proposed analogy, to the interpersonal resolution of unmarked speaker/claim attribution, and seven testable propositions are derived from the resulting account. The framework offers organisational communicators, leaders, and designers of human-AI interaction a mechanism-level explanation of when and why hedged language builds trust rather than undermining it, together with the boundary conditions under which personal anchoring ceases to buffer a low stated likelihood.

Beyond tools: Reviewing AI’s role in second language speaking skills and implications for classroom practice

Zhong Linling — Fri, 07 Aug 2026 00:00:00 -0500

This review examines the transformative role of artificial intelligence (AI) in second language (L2) speaking instruction, analyzing its pedagogical potential and implementation challenges. It synthesizes findings from recent empirical studies to demonstrate how AI technologies, including automatic speech recognition (ASR), intelligent tutoring systems, and conversational agents, are reshaping L2 education. The analysis reveals that AI effectively addresses traditional limitations in speaking instruction, such as limited practice opportunities and delayed feedback, while also helping to reduce learners' speaking anxiety and improve fluency and pronunciation. However, the review also highlights critical considerations, including the variability in effectiveness across different AI applications and the need to balance AI-mediated support with human interaction. By exploring both the theoretical underpinnings and practical applications, this study provides evidence-based guidance for educators and researchers seeking to leverage AI to enhance L2 speaking competencies. It concludes that future efforts should focus on strategic integration to transition AI from a supplementary tool to an integral component of comprehensive language education.