Digital

The authors would like to make the following corrections to the published paper [...]

This paper investigates the use of large language models (LLMs) as evaluators in multidimensional machine translation (MT) assessment, focusing on the English–Indonesian language pair. Building on established evaluation frameworks, we adopt an MQM-aligned rubric that assesses translation quality along morphosyntactic, semantic, and pragmatic dimensions. Three LLM-based translation systems (Qwen 3 (0.6B), LLaMA 3.2 (3B), and Gemma 3 (1B)) are evaluated using both expert human judgments and an LLM-based evaluator (GPT–5), allowing for a detailed comparison of alignment, bias, and consistency between human and AI-based assessments. In addition, a classroom calibration study is conducted to examine how rubric-guided evaluation supports alignment among novice evaluators. The results indicate that GPT–5 exhibits strong agreement with human evaluators in terms of relative quality ranking, while systematic differences in absolute scoring highlight calibration challenges. Overall, this study provides insights into the role of LLMs as reference-free evaluators for MT and illustrates how multidimensional rubrics can support both research-oriented evaluation and pedagogical applications in a mid-resource language setting.

This article presents a comprehensive bibliometric analysis of the indexed academic literature on the application of distributed ledger technology (DLT) and blockchain in the tourism industry. Using the bibliometrix library within the RStudio environment, key bibliometric indicators were examined in order to characterize the evolution, structure, and thematic focus of this emerging field of research. The systematic literature review, which adhered to PRISMA guidelines, involved retrieving publications from the Web of Science and Scopus databases. A curated dataset of 100 relevant documents was identified and analyzed in terms of annual scientific production, leading journals, influential authors, and highly cited publications. The results indicate that blockchain technology dominates the literature, with a strong emphasis on its potential to enhance trust, transparency, and efficiency in tourism-related processes. In particular, identity management, secure transactions, and disintermediation emerge as central research themes, reflecting blockchain’s capacity to support decentralized, immutable, and privacy-preserving interactions between tourists and service providers. Overall, the findings reveal a rapidly growing and increasingly structured body of knowledge, highlighting emerging research directions and technological challenges for future studies on DLT applications in tourism.