A Transitivity Analysis of Two Political Articles: An Investigation of Gender Variations in Political Media Discourse
Bibliographic record
Abstract
The purpose of the present study is to use transitivity analysis to investigate gender variations in political media discourse from the point of view of male and female columnists. To attain this purpose, this paper adopts a critical discourse analysis (CDA) of Halliday’s theory of transitivity process types. Literature shows that gender variation is one of the key elements affecting language. However, variation in gender studies, particularly in article writing, has not been recently addressed in relation to transitivity analysis. To scrutinize types of transitivity involving material, mental, relational, verbal, existential, and behavioral processes, qualitative and quantitative methods were deployed to achieve a deep understanding of transitivity process types. Paired Sample T-Test has also been employed to investigate whether there is a significant difference in gender variations pertaining to frequencies of process types of transitivity. The results revealed that the material process has been the most frequent process used by female and male political columnists and has highly dominated the discourse in both articles. In addition, the finding showed that there is no significant difference in gender variations pertaining to the frequencies of process types of transitivity. Analyzing participants’ roles and circumstances elements could provide more pertinent data for further research.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".