Bibliographic record
Abstract
The media’s tendency to widely represent conflicts, through legitimization or de- legitimization, makes us believe that media narratives may not be perceived as ‘neutral stances’ for the public consumption. This study investigates the policy of a mainstream newspaper, The News International (NI), in Pakistan and discursive strategies manipulated by its editorial writers to portray the Arab Spring. It, specifically, examines how the selected newspaper editorials thematically constructed the uprising; (re) formulated the public opinion by echoing the Arab Spring-centred perspectives; and mostly backed the revolting protesters against the dictatorial rules in Tunisia, Yemen, Egypt, Libya and Syria. The data consist of purposefully selected editorials, which were published between January 2011 and December 2012. This particular timeframe has been distinguished for peak media coverage of the events. The qualitative data (editorial content) are analysed by using NVivo. Through discourse analysis approach, it is revealed that editorial writers employed several recurrent themes (e.g., protests, democracy, horror) to project a positive image of the protesters’ movement and fully utilised their prerogative in constructing a ‘pro-Arab Spring’ discourse. This study concludes that such ‘opinion discourses’ serve as an eye-opener to the role of media in representing conflicts from different angles while staying in different societies. It also provides insights into the ways newspapers (dis) empower readers by promoting certain factions of a conflict and devaluing others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".