Sustainable Green Future from a Multimodal Positive Discourse Analysis Perspective: Investigating Environmental Metaphors in Some Selected Cartoons
Bibliographic record
Abstract
A substantial body of research pertaining to ecological issues has significantly centered its attention on laying out the deleterious, harmful and destructive consequences stemming from human actions upon the environment. This emphasis often revolves around negative metaphors that evoke fear, threats, and danger, leaving the positive aspects of green and sustainable future relatively unexplored. This paper adopts a Multimodal Positive Discourse Analysis (MPDA) approach to scrutinize visual socio-cognitive metaphors presented in 11 environmental cartoons disseminated by the Indian Council of Energy, Environment and Water (CEEW) on diverse ecological issues. The study aligns with Forceivelle's (1996 & 2009) theory of multimodal metaphor and Hart's (2008) model of critical metaphor analysis in the light of Martin's (2004) Positive Discourse Analysis (PDA) theoretical framework. Therefore, emphasizing the significance of visual metaphor in the formulation of new and positive eco-friendly scenarios through the process of projection and integration across two distinct input spaces. The results of the study show that positive scenarios such as hope, success, aspiration, ambition, and optimism have been motivated in the generic space, providing an antithesis to the predominant negative scenarios characterized by stereotypes of eco threat, harm, insecurity, and instability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".