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All Journal Indonesian Journal of Language and Economic Discourse Indonesian Journal of Computational Language Studies
Ainul Hadziqi
Universitas Sarjanawiyata Tamansiswa

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Discourse of economic inequality in Indonesian news media: a sociolinguistic perspective Ainul Hadziqi
Indonesian Journal of Language and Economic Discourse Vol. 1 No. 1 (2026): Language, power, and economic narratives in Indonesia
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/ijle.v1i1.523

Abstract

Background: Economic inequality remains one of Indonesia’s most pressing socio-economic challenges, yet its representation in news media has rarely been examined through a sociolinguistic lens. Objective: The objective of this study is to analyze how Indonesian online news media discursively construct inequality, how linguistic variation reflects social stratification, and how framing strategies shape causal attributions and proposed solutions. Method: Methodologically, this research applies a qualitative design by combining Critical Discourse Analysis, Sociolinguistic Analysis, and Framing Analysis to a purposive corpus of 55 online articles from 2022 to 2024. Results: The results show three key findings: first, news texts tend to legitimize inequality by individualizing poverty and externalizing causes through references to global crises; second, lexical stratification marks elite actors with technical jargon while grassroots voices are reduced to emotive anecdotes; third, framing emphasizes state-centered solutions such as subsidies and welfare while marginalizing community-based alternatives like cooperatives. Implication: The implication of this study is that Indonesian news media not only mirror but also filter public discourse on inequality, privileging elite narratives while limiting democratic deliberation on structural reforms. Novelty: This study uncovers how Indonesian news media linguistically construct and frame economic inequality in ways that legitimize dominant perspectives while constraining alternative narratives of structural change.
Small models, many languages: parameter-efficient adaptation of multilingual language models for low-resource Indonesian languages Ainul Hadziqi
Indonesian Journal of Computational Language Studies Vol. 1 No. 2 (2026): Large language models, linguistic diversity, and responsible NLP in Indonesia
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/ijcl.v1i2.916

Abstract

Background: Multilingual language models have expanded computational access to many languages, yet Indonesian local languages remain unevenly represented in corpora, benchmarks, and adaptation pipelines despite Indonesia’s exceptional linguistic diversity. Objective: This study examines how parameter-efficient adaptation can support small multilingual language models for low-resource Indonesian languages by treating corpus adequacy, source provenance, language coverage, and evaluation readiness as central methodological conditions. Method: Using a corpus-level research design, this study maps 20 public source-level items consisting of task datasets, benchmark resources, documentation, registry infrastructure, supplementary corpus portals, and model-context collections for Indonesian and selected local languages. Results: The findings show that task-ready datasets form the strongest part of the corpus frame, while benchmarking, reproducibility, and auxiliary corpus layers remain less evenly distributed. Language coverage is stratified, with Javanese and Sundanese occupying stronger cross-task positions, while Acehnese, Ngaju, Bima, Toba Batak, and Ambonese Malay are better treated as focused transfer-stress cases. Implication: These patterns indicate that parameter-efficient fine-tuning should not be interpreted through model efficiency alone, because adaptation gains remain bounded by corpus distribution and language-specific evidence. Novelty: The novelty of this study lies in repositioning PEFT for Indonesian local languages as a corpus-dependent multilingual adaptation problem rather than a purely architectural optimization problem
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