Exploring Edward Said’s Journalistic Collocations in Al-Ahram Weekly Newspaper: A Corpus-Based Approach
Bibliographic record
Abstract
This paper explores Edward Said’s journalistic collocations as a discursive practice of the social actors that Said frequently referred to in Al-Ahram Weekly newspaper. Towards this end, a corpus-based approach has been utilized in a methodological synergy that combines the corpus techniques of extracting keywords and calculating collocations as well as the qualitative method of analysing social-actor representations (Van Leeuwen, 1996, 2008). The data used for analysis comprise a corpus of virtually all the articles written by Said in Al-Ahram Weekly from 1998 till 2003. The corpus is 105,031words and has been electronically manipulated by the corpus software tools of Wmatrix (Rayson, 2003) and WordSmith (Scott, 2012). The paper has reached three findings. First, Said’s journalistic discourse in Al-Ahram Weekly revolves around 38 social-actor keywords that reflect his thematic foci all through the time span he was writing articles for the newspaper. Second, of all these keywords, only twelve node words have been identified to associate with peculiar collocates; the node words were divided into (1) nominations of political personas and (2) genericizations with various discourse functions. Third, Said’s peculiar collocations reflected his ideological orientations towards certain political personas and specific topics in his journalistic discourse.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".