The Portrayal of Environmental Concerns: An Ecolinguistic Analysis of Media Discourse
Bibliographic record
Abstract
The global focus on environmental/climatic concerns and sustainability has intensified in recent years due to the growing recognition of the urgent need for action to mitigate and adapt to climate change. As one of the key goals of Saudi Vision 2030, the Saudi Green Initiative strives to mitigate the effects of climate change and improve biodiversity through multiple measures including promulgating awareness and responsibility in the general public. With this respect, undoubtedly media plays a crucial role in shaping public opinion and influencing policy decisions. Therefore, it is essential to investigate how environmental issues and sustainability are portrayed in media discourse in the Saudi context. This research intends to analyze media representations of environmental concerns specifically in English newspaper discourse using an eco-linguistic framework. For the said purpose, the corpus developed was based on content published in Arab News an English newspaper, and analyzed through Sketch Engine and LancsBox6.0 software. The key frames that emerged were evaluated and interpreted both statistically and qualitatively to ascertain the role of media in imparting and creating collective consciousness regarding the need for sustainable environmental actions. The study demonstrated that the newspaper discourse portrayed the climate concerns effectively by framing the climate as a change, crisis, fight, and collective social responsibility through discursive strategies such as frequent repetition of concepts, specific lexical choices like negative, strong expressive words as well as techniques of naming and making concrete references. Additionally, the writers suggested practical solutions and measures people and nations must need to take collectively to address the ever-growing climate challenges.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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 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".