COVID-19 Translated: WHO’s and the City of Ottawa English and Arabic Narrative of the Pandemic
Bibliographic record
Abstract
Many national and international organizations have constructed and disseminated COVID-19 public narrative and have contributed into the construction of the pandemic meta-narrative. The World Health Organization (henceforth WHO) is the international organization responsible for publishing COVID-19 related information to global audiences and citizens. However, the City of Ottawa is the Canadian federal capital responsible for disseminating information about the pandemic to its citizens who reside in Ottawa. In the light of the evolving events, this paper explores the English and Arabic public narrative of COVID-19 as constructed and published by WHO on its multilingual website and by the City of Ottawa Public Health in Canada. In specific, it scrutinizes how similar or different the communicative messages and meanings are as embedded in the English and Arabic texts of the pandemic public narrative. To do so, this paper methodologically utilizes a qualitative narrative analysis research design guided by narrative theory, types of narrative, and narrative features. Accordingly, the corpus consists of 48 English texts and their 48 Arabic translated texts which were published on WHO’s website under two COVID-19 pandemic topics: “Myth busters” and “Q & As on coronaviruses (COVID-19)”. As well, this paper analyses 12 English texts and their 12 Arabic translated texts which were published on the City of Ottawa’s website. The findings of this paper provide further understanding of similarities and differences in communicative messages and meanings as embedded in the English and Arabic public narrative of COVID-19 pandemic, and as published and represented by WHO and the City of Ottawa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".