The Effect of Climate Change Semantic Expressions on Perceptions and Attitudes Towards Decarbonisation
Bibliographic record
Abstract
This paper is proposed to clarify the effectiveness of semantic expressions used to designate climate change in France context, i.e. “réchauffement climatique” (“global warming”); “changement climatique” (“climate change”); and “derangement climatique” (“climate imbalance”). An experimental study (sample size N = 126) based on ‘linguistic semantics’ approach is conducted in order to assess the effect of these expressions on concerns, perceptions risk and sensitivity regarding Climate Change (CC). Our results show that the expression “réchauffement climatique” (“global warming”) is the most appropriate from a statistical standpoint. It increased the importance of the problem (salience of this issue) relative to other societal issues (e.g. unemployment, social justice, crime, etc.); it also enhanced participants' sensitivity (respondents' emotions associated with CC) more than the other expressions. We can still note however a strong difference in impact among the expressions if we were to calculate their impact on the basis of risk perception and communication objective. Results showed that when focusing our communication campaigns on nature, it would be preferable to use the term “changement” ("change"), when focusing our communication on social level, it would be preferable to use the term “réchauffement” ("warming"), whereas the term “dérèglement” ("imbalance") becomes the most suitable in seeking to build a communication campaign focusing on economic aspects. Semantics therefore should be selected depending on the communication objective.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".