The Impact of COVID-19 Pandemic Linguistic Landscapes on Lifestyle, Health Awareness and Behavior
Bibliographic record
Abstract
The recent COVID-19 pandemic created a plethora of new challenges for the world and affected all aspects of human life. This research aimed to look further into the sociolinguistic aspects of the COVID-19 Linguistic Landscape (LL) and assess the extent to which public signs affected people’s behaviors and lifestyles during the COVID-19 outbreak in the Saudi context. A semi-structured questionnaire was developed to collect data related to the study. A total of 215 participants from different regions of Saudi Arabia participated in the survey. The study results provide evidence of language as a critical element in reflecting the social realities of the Saudis. The data confirmed that the COVID-19 Linguistic Landscape (CLL) served several functions at both individual and institutional levels in Saudi Arabia. Key findings emerged about the role of the linguistic landscapes set up in public spaces in changing people’s thoughts and behavior as well as how they reacted to urgent and exceptional conditions such as COVID-19. In sum, the pandemic-associated signs led to remarkable positive changes in the daily routine of people.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".