Evaluating Literary Meaning: Appraisal and Argumentation in Dancing in the Dust
Bibliographic record
Abstract
This study evaluates literary meaning using appraisal theory and argumentation analysis in Kagiso Lesego Molope's novel, Dancing in the Dust (2002). Through qualitative analysis, it explores the interplay of appraisal resources and argumentation strategies. The research provides an overview of appraisal theory and argumentation analysis, highlighting their relevance in literary analysis. By closely examining the novel, it identifies diverse appraisal resources such as Affect, Judgment, and Appreciation, analysing their relation to themes, character development, and social commentary. The study also explores the author's argumentation strategies, including logical reasoning, evidence, and rhetorical devices. The findings deepen the understanding of how literary meaning is constructed through appraisal and argumentation, shedding light on artistic choices, character portrayals, and socio-cultural commentary. This research enhances appreciation of Molope's novel by revealing evaluative and persuasive techniques, emphasising their importance in literary analysis and their role in language, literature, and meaning construction.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".