From Eco-Jihad to Politicization: A Corpus-based Eco-linguistic Discourse Analysis of the Arab Media Coverage of the Safer Floating Oil Tanker
Bibliographic record
Abstract
This study attempts a corpus-based discourse analysis of the coverage of the FSO Safer in Arabic media to determine the main recurrent categories and themes in the coverage produced by outlets associated with conflicting/warring parties, as well as reports from more neutral media. This study, therefore, provides analysis of media coverage on the FSO Safer starting with the first report from Aljazeera on the issue in 2019, until June 2022. This study utilized Sketch Engine to compile a digital corpus of Arabic news articles. The corpus consists of 420,00 tokens. Additionally, this study explores how the word al-bīʾah (environment) is represented in the corpus by conducting a transitivity analysis of each concordance line or clause that included the word or one of its variants. This study also examines the various manipulative strategies that media outlets associated with conflicting parties used to determine how each presented the 'other', holding them accountable should the catastrophe strike. The findings of this study indicated that the themes recurrent in the corpus include the scale of catastrophe, environmental damage in the event of a spill, economic consequences, the UN emergency plan, echo-jihad, and 'we' vs 'them', among others. The presence of the word al-bīʾah in the corpus clearly shows that human beings are represented as the most active of beings; those who think, do and act in the world and those who behave and speak. Inanimate objects, on the other hand, are represented as passive participants; things are done to them.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".