Emak-Emak Representation in Start-Up Ads: Verbal and Non-Verbal Analysis of Indonesian Moms
Bibliographic record
Abstract
Women stereotyping is always interesting to be discussed. This paper is concerned with verbal and non-verbal meanings to portray Indonesian moms, or Emak-Emak represented in two official videos of GoMart and GrabMart. This study owes the framework of Halliday’s transitivity system (2014) to analyse the verbal meaning and Kress & Leeuwen’s interpretation of visual grammar to analyse the non-verbal meanings. The outcome of the verbal analysis using transitivity shows that the procedures used by GrabMart and GoMart are focused differently. While GrabMart focuses more on the material process of “doing” rather than “happening”, GoMart tends to emphasis on the relational process of “attributive” and primarily on “identification”. GoMart portrays Emak-Emak's stereotypes more objectively, whereas GrabMart is more subjective to shape the viewer's perception of them. This study offers recommendations for future research by taking a comprehensive approach to the phenomenon of gender stereotyping in online audio-visual advertisements, analyzing the interaction of verbal and non-verbal meanings in various advertisements, thoroughly examining the relevant stream of metafunctions, and proposing ideas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".