Adjectival Evaluation of Women in Pakistani English Newspaper Coverage of the Aurat March: A Corpus-Assisted Appraisal Analysis
DOI:
https://doi.org/10.58932/MULK0010Keywords:
corpus linguistics, Appraisal Theory, Aurat March, adjectival evaluation, lexical primingAbstract
Evaluative adjectives can never be purely descriptive. In media discourse, they carry the weight of evaluation, forming popular opinion by giving properties to people and events which are hardly neutral. This outcome may often lead to consequences when the topic of discussion is feminist political activism within a disputed public space. This study approaches such outcome from a corpus-based Appraisal of adjectival evaluation in the English language Pakistani newspaper reporting on the Aurat March, the most prominent modern feminist mobilization in the nation. A corpus of 390 articles that contain 278,206 tokens published between 2018 and 2026 was analysed. It employs Systemic Functional Linguistics Appraisal Theory, specifically the Attitude subsystem, to examine adjectives modifying two levels: the person level, focused on woman/women, and the event level, focused on Aurat March. SpaCy dependency parsing , Mutual Information collocation statistics , systematic enforced manual Appraisal coding with a principled exclusion taxonomy are all methodological tools. The analysis shows that there are three evaluative discourses which coexist. A discourse of celebration of resistance is anchored in positive Judgment adjectives of Tenacity and Capacity. Negative Affect adjectives are what propel a victimhood and vulnerability discourse. A two-level moral condemnation counter discourse operates through statistically marked Social Sanction adjectives at the person level and high frequency negative attitudinal adjectives at the event level. The study is informed by both the Lexical Priming theory and the scholarship of gender and language, which reveals that the surface neutrality of the corpus represents a stratified evaluative system with substantial consequences.