Symbolic Portrayal of Social Classes in Pakistani Advertisement
Bibliographic record
Abstract
The current study aims to identify particular ways through which social actors are represented by Pakistani media such as MCB (Muslim Commercial Bank) Ladies Account (2017). This study is only limited to two Pakistani advertisements as a sample of study. The research design is qualitative content analysis. The study seeks to examine the propagation of class differences for the sake of gaining viewer’s empathy in order to achieve marketing purposes. The researchers have applied Leeuwen’s (2008) framework of Visual Representation of Social Actor for the analysis of data. The analysis of data has provided an insight into different ways class differences are showcased. It has further provided an insight that notion of lower/middle class is constructed and represented as “others” in the particular advertisements with the help of Visual Representation of Social Actors. The result of study validates that lower/middle class is particularly marginalized in the mentioned advertisements, whereas it has become a general practice of Pakistani media to project such kind of class dichotomy. The study has further incorporated the idea that through such kind of projections the capitalists propagate the purchase of unwanted items. Wherein, regardless of any use the viewers while empathizing with the social actors purchase the advertised items.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".