A Deictic Analysis of Pakistan’s National Identity Representation in the Indigenous English Newspapers
Bibliographic record
Abstract
The goal set for the present study is to investigate the temporal and spatial deixis which are used to represent and build the national identity of Pakistan in the two leading indigenous English newspapers. The study investigates how the WAR and NATION frames are used in the opinion articles of The News and DAWN to project the negative image of Pakistan by using temporal and spatial deixis. The data collection is based on the ten years of opinion articles from 2007–2017. The political situation of Pakistan in the year 2007 was crucial and it is marked as a step towards the revival of civilian democracy through the announcement of general election in Pakistan during the ongoing war on terror. Purposive sampling technique is used in the selection of data. The theoretical foundation is based on the Anderson’s (1991) Imagined community. The empirical framework is based on Harts’ (2014) Critical Cognitive Discourse Analysis. Both qualitative and quantitative methods are employed. Antconc is used to generate the frequencies and concordance lines of the text. The data analysis shows that journalists mostly projected the negative image of Pakistan by utilizing different deixis and by linking the events of past from shared memories with the present and the future events by conceptualizing WAR and NATION Frames.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".