Emotions and Religions: Media Representations and Visual Metaphors of Emotions on the World Wide Web
Bibliographic record
Abstract
The article analyses two research problems related to media representations (digital lexicons of emotions) and visual metaphors of emotions in the Internet materials concerning religions during the pandemic. For the analyses two research techniques were used: analysis of the content and analysis of visual metaphors. Using the key words ‘religion’ and ‘coronavirus’ yielded 100 natural search engine results from Google Search (organic search), coming from all over the world, e.g. UK, USA, Poland, Canada, India, Israel, Iran, Qatar, North Korea, subject to content analysis. The research material employed in the study of metaphors are selected illustrations listed in an international catalogue of photographs and illustrations involving religious motifs, available during the COVID-19 pandemic. The metaphor analysis covered the illustrations selected from the study data, with the richest visual semantics and meeting at least one of the definition requirements for a visual metaphor. On the basis of the analyses of virtual lexicons of the emotions of fear (and accompanying distrust), anger/wrath, happiness/joy (and accompanying: hope, trust, satisfaction, mental balance, peace of mind, closeness, solidarity, compassion, care, solace), and sadness/uncertainty, a general conclusion was formulated about the enrichment of the emotional media culture with new contexts of language use. The described examples of visual metaphors of emotions allowed for the reading of compositional techniques, symbolism, colours, contrast and elements taken from the original metaphors. The results contribute to the studies of the social concept of emotions presented in the digital media and of new contexts of their media representations. They point to the adjustment forms of the organization of social life on the Internet initiated by religious practices that are, by their nature, emotional. The media representations and visual metaphors of emotions contribute to the creation of universal lexicons of emotions.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".