The Representation of Afghan Refugees in Pakistani Urdu and English Blogs: A Corpus-Assisted Discourse Analysis
Bibliographic record
Abstract
Afghan refugees are considered the largest refugee population, living across borders in countries like Pakistan, Iran, the United Kingdom, European Union countries, the United States, Canada, Gulf countries, and India. Pakistan and Iran have hosted the most enormous number of Afghan refugees since the Soviet invasion in 1979. Since their arrival in the host countries, their presence has always been the topic of debates and news reports. In this regard, the current study is designed to understand the behavior of Pakistani Urdu and English blogs to see how Afghan refugees have been represented. The current study offers insights into the image of Afghan refugees created in the third space of digital media following the technique of corpus-assisted discourse analysis by looking at the linguistic context, concordances, and collocates of the lemma “Afghan” in the collected corpus of online Pakistani blogs. Similarly, the current study has identified negativity associated with Afghan refugees using lemmas such as Haram Khor, smugglers, drugs, burden, and terrorism.
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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 | 0.006 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".