What if Nature Fought Back? Multimodal Metaphor in Green Non-Profit/Social Advertising
Bibliographic record
Abstract
Creating a conscious environment culture which highlights our commitment to protecting natural resources has been steadily increasing. The use of multimodal metaphor is a crucial rhetorical device for understanding the complexities of the global climate change crisis. More specifically the exploitation of multimodal metaphor in genres such as public awareness campaigns aims at triggering the audience’s attention. In light of this, this paper investigates the use of multimodal metaphor in green non-profit/social advertising to understand to what extent these advertisements may be effective when promoting and encouraging change in behaviour patterns concerning environmental protection and climate change. For this purpose, a corpus of 130 multimodal advertisements containing metaphors on environmental awareness has been investigated. The study uses an adaptation of previous procedures designed for the identification of verbal, visual and multimodal metaphor (Alousque, 2014; Hidalgo-Downing & O’Dowd, 2023; Pragglejaz, 2007; Šorm & Steen, 2018; Steen et al., 2010). Results confirm the effectiveness of metaphorically conceptualising environmental themes such as climate change, global warming, and pollution. Moreover, the findings show that the pragmatic effect of metaphor used by non-profit organisations aims at creating a conscious environment culture, while the majority of the metaphors suggest a mixed or negative evaluation of environmental issues. Further research will include the investigation of metonymy in these campaigns.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".