Conceptual Metaphors for Covid-19 : An Analysis of Metaphors for Covid-19 in the Discourse of Political Leaders of the UK, the USA, Canada, and Australia
Bibliographic record
Abstract
Since the emergence of Covid-19 in December 2019, metaphors to talk about the pandemic have been extensively used in political discourse. This study aims to compare metaphors for Covid-19 in the discourse of political leaders of the UK, the USA, Canada, and Australia by drawing upon three conceptual metaphors found by De la Rosa (2007). The following conceptual metaphors are investigated: DISEASE IS A WAR, DISEASE IS A NATURAL FORCE, and DISEASE IS A JOURNEY. To find metaphors for Covid-19, one corpus of transcribed political discourse was compiled for each country. The corpora were then searched using lemmas of words specific to each conceptual metaphor. By drawing upon conceptual metaphor theory (Lakoff & Johnson, 1980) instances of metaphor usage were then analyzed. The frequency results showed the natural force metaphor to be the more frequently used in the UK and Canada corpora. In contrast, no occurrences of natural force metaphors were found in the USA or Australia corpora. The war metaphor was most frequently used in the USA corpus, and in the Australia corpus, the war and journey metaphor were used at similar frequencies. The findings of this study indicate that there is a difference in both frequency and choice of conceptual metaphors between the four corpora. The analysis also suggests that different metaphors can be used for different purposes.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".