Representation(s) of Developed and Developing Countries in Newspapers’ Coverage of Climate Conferences: A Critical Discourse Analysis
Bibliographic record
Abstract
This study critically analysed how developed and developing countries were represented in The Independent and The New York Times’ coverage of the Conferences of the Parties to the UNFCCC between 2004 and 2013. The method of analysis was a qualitative critical discourse analysis in accordance with Fairclough’s (1989) framework with the support of corpus techniques.The research findings showed that there were distinct responsibilities for climate change ascribed to the developed and the developing countries. While the developed countries were represented as being reluctant and indifferent towards their responsibility, the developing countries tended to depend on the developed countries’ support in solving their climate-related problems. During the study period, therefore, no consensus could be reached on a common framework for climate change. The linguistic features of lexical choice, passivisation, nominalisation, modality and metaphor were found ideologically employed in the newspapers’ representations of the countries. Additionally, the ideologies and their linguistic manifestations were influenced by the media’s discursive practices and the wider social context.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".