Power and Voices of Authority in the Media Narrative of Malaysian Natives: Combining Corpus Linguistics and Discourse Analysis Approaches
Bibliographic record
Abstract
Orang Asli is a group of indigenous people who live according to a set of lifestyle and belief system and speak the native language. Previous studies have found that the Orang Asli are mostly depicted in the news media as backwards and dependent on others (Marlina Jamal & Shakila Abdul Manan, 2016). Such stereotypical depiction is shared with other research undertaken in various countries, particularly those in Canada or Australia. While many of these studies analysed findings from the media and communication or socio-cultural perspectives, the present study examines the representation of Orang Asli in Malaysian news/media by focusing on the use of language, i.e. linguistic viewpoint. The examination undertaken in this study focuses on headlines and lead paragraphs of one of the most influential English language newspapers in Malaysia. This study demonstrates the synergy of two methodological approaches in linguistics namely critical discourse analysis and corpus linguistics. Our main findings show how Orang Asli is consistently depicted around stereotypical news narratives such as issues of land rights and other resources. We also found that the voices of Orang Asli leaders are overshadowed by other authorities such as the state government. Thus, this study contributes to shed light on the linguistics resources and language patterns used to portray the Orang Asli in Malaysian newspapers. Keywords: Orang Asli; news discourse; corpus linguistics; critical discourse analysis; indigenous people
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".