Linguistic Representation of Ideological Strategies in Two Iranian Newspapers Written in English
Bibliographic record
Abstract
The present study investigates ideological strategies mainly embodied in X-phemism in light of Van Dijk’s and Charteris-Black’s frameworks of the construction of reality in news reports. To this aim, a representative sample of 239 news reports was selected for a one-year period (end of January 2010 to end of January 2011) from two Iranian newspapers published in English—the Tehran Times and Kayhan International. A total of 11,938 strategies from 10,676 clauses in these newspapers were analyzed, both quantitatively in terms of frequency of occurrence for each strategy, and qualitatively for the reason of occurrence. Findings have revealed that both news outlets contain a wide range of ideological strategies, among which Objectivity with its sub-strategies, Negative and Positive Lexicalization as part of metaphor, and Exaggeration/Hyperbole were the most frequent whereas Counterfactuals, Openness/Honesty and Irony had the lowest frequencies. The quantitative and qualitative findings are discussed in the result and discussion sections.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".