Climate Change and Linguistic Assistance to Overcome Its Risks: An Eco Linguistic Analysis of Greta Thunberg Speech
Bibliographic record
Abstract
Climate change, like many other pressing issues in today's society, is being scrutinized by the linguistic community as well under the subcategory of Eco linguistics. In this context, the present study tries to illuminate this new field by critically or Eco linguistically analyzing Greta Thunberg’s most recent (selected) speech. In particular, by using the theoretical framework given by Norman Fairclough’s three-dimensional discourse model, the study analyzes the way how Greta draws on different linguistic devices or adopts highly selected use of language to convey an ecologically beneficial perspective and ideology. It also examines the use of figures of speech in her speech and it explores how the selection is made for conveying an ecologically beneficial perspective. The findings revealed her careful selection of vocabulary items, use of direct and dominant language, extensive use of metaphors, ironic expressions and lack of euphemistic expressions. Besides that, her use of conjunctions adheres to coherence, her inductive style of argumentation focuses on reasoning. The findings showed the use of transitions throughout her speech between pessimistic and optimistic expressions. The result presented that her manipulation through the use of sensitive and emotionally triggered words have influenced people to a great deal to the point of shaping their distinct ideology regarding climate change and thus to pursue an agenda based on action. The study also provides implications to develop understanding of climate related discourses as it stresses the role which language or linguistics play in encouraging and influencing people to safeguard the systems that sustain life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".