An Investigation of the Interaction Markers of Pakistani Journalistic Discourse from Gender Perspective
Bibliographic record
Abstract
The objective of this research is to investigate the language used by male and female Pakistani journalists by focusing on the use of interaction markers. This study aims to explore the meta-discourse features in the writings of the Pakistani English newspaper journalists. The data is collected from Dawn, The News, The Nation and The Express Tribune newspapers. The corpus for the research consisted of two hundred (200) columns written by forty Pakistani journalists including both males and females. Hyland’s (2005a) model of interactional meta-discourse was used as a theoretical framework. Mixed methodology will be used to analyze the data qualitatively and quantitatively to find out the gender-based differences in the use of interaction markers in the writings of Pakistani journalists. First, the data collected are quantified quantitatively then for the elaboration of gender-based differences in the use of interaction markers, qualitative research methodology is used. Moreover, Antconc, a corpus-based research tool, is employed to statistically analyze the corpus of the study. The study provides the analysis of interactional markers in the Pakistani journalistic discourse by employing Hyland’s (2005a) model of interaction. The results show that there exists a gender-based difference in the use of interaction markers. The female Pakistani columnists use interaction markers more frequently than the male counterparts. The research provides new insight to the national and international researchers about gender-based differences in media discourse within the Pakistani context.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".