Ethical Guidelines and News Reporting in Pakistani English Newspapers: Critical Stylistics and Corpus Linguistics Analysis
Bibliographic record
Abstract
Media is a potent source of disseminating news in the present era. So, news regarding violence against Women (VAW) is not an exception. The objective of the present study is to examine how news of VAW is portrayed in the English newspapers of Pakistan. The patterns to portrayal of news regarding VAW are based on gendered landscapes through the predictable images of gender identities, which ultimately leads to inequality and biasness against women. The present study investigated the news regarding VAW published in the newspapers. VAW is also generally known as domestic violence. The researchers conducted a discourse analysis of five English newspapers and collected 510 news reports regarding VAW published during 2017 and 2018. In order to make the findings objective and replicable, the researchers used Critical Stylistics (Lesley Jeffries, 2010a) and Sketch Engine, a computer-based corpus software. Findings suggested that the ethical guidelines proposed by various journalist organization were flagrantly violated. These violations involve revealing the victims’ identity i.e., age, location, and even slantingly holding the victims responsible for the violence. The researchers discovered that these unethical practices may result in inappropriate and stereotypical narratives about women. Therefore, it becomes necessary for the media to uphold the ethical considerations to assure the appropriate understanding of VAW and its severity.
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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 | 0.019 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".