Critical Metaphor Analysis of Climate Change in COP28 Speeches: An Ecolinguistic Perspective
Bibliographic record
Abstract
Climate change has emerged as a significant worldwide concern in recent years and has taken center stage in political discourses. In political speeches, metaphors are commonly used to communicate this scientific issue to the public, with the speakers’ attitudes conveyed through them. From this starting point, the current study examines metaphor construction of climate change in thirty-two speeches by political leaders at the 28th Conference of the Parties (COP28) to the United Nations Framework Convention on Climate Change held from November to December 2023. Based on the conceptual metaphor theory, this study applies the framework of critical metaphor analysis and further evaluates metaphors from an ecolinguistic perspective. It is found that multifaceted metaphorical keywords and conceptual metaphors are used in constructing the key concepts in climate change, with the war, force, living being, vehicle, journey, building, commodity, and greenhouse metaphors as the most prevalent ones identified in this study, and they possess various pragmatic purposes in contexts. From the ecolinguistic perspective, metaphors identified in these speeches are generally eco-friendly, building a positive and progressing image of dealing with climate change by these political leaders and nations. This study confirms the crucial function of metaphors in political speeches on climate change to communicate information and influence the audience’s perception of this issue.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| 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".