CRITICAL DISCOURSE ANALYSIS OF THE EFFECT OF FIGURES OF SPEECH IN SEVERN SUZUKI’S PERSUASIVE SPEECH
Bibliographic record
Abstract
Critical Discourse Analysis (CDA) is a frequently used method with the intent of serving a trustworthy evaluation of what is intended to mean when the language is used to define and commentate. It is therefore of capital importance to consider the social context, the manner and word selection while analysing a speech in order to avoid passively reporting upon since the speech is impregnated with its meaning and perspective. In this respect, the purpose of the current study was to search for the critical discourse analysis of the speech given by a then 12-year-old Canadian girl called Severn Suziki, an environmental activist, in United Nations Conference on Environment and Development (UNCED) in 1992 in order to draw the attention of 117 presidents and representatives of 178 nations to some crucial topics such as environment and global warming. The keyword analysis of the speech revealed that the most frequently used words were ordered as child, children, world and afraid confirming the main aim of the speech that the environment should be protected for the future generations. Critical discourse analysis of the speech demonstrated that Severn Suziki utilised 7 figures of speech such as alliteration, hyperbole, imagery, irony, parallelism, rhetorical questions and simile justifying that she had her own particularity and implemented various persuasive techniques and figures of speech.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".