A Critical Corpus- Based Analysis of the Words Muslim and Islamic Vs. Christian in Contemporary American English
Bibliographic record
Abstract
The words Muslim and Islamic have recently become a recurrent theme in western media especially in the U.S. However, there is little research on how the words Muslim as opposed to Christian are represented in the US spoken and written media discourse. Utilizing the Corpus of Contemporary American English (COCA), the current study investigated how Muslims and Christians are portrayed in U.S media outlets through a quantitative and a qualitative analysis of the lexical collocations of the words Muslim, Islamic and Christian. A threshold of Mutual Information (MI) score of at least 3. and 2% frequency was set for the candidate collocates. The results showed that the former group was largely associated with fanaticism and ethnicity while the other group was largely associated with knowledge and theology. A fine-grained analysis of a common collocate i.e., fundamentalist revealed striking differences between the characteristics of Muslim fundamentalists and Christian fundamentalists in US media. The study highlights the value of corpus-based approaches in enhancing the objectivity of critical discourse analysis and pinpointing the lexical and grammatical patterns that contribute to biased mental construction of particular groups.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".