A Corpus Linguistic Analysis of Characterisation in Chimamanda Ngozi Adichie’s Americanah
Bibliographic record
Abstract
Research in Corpus Linguistics has provided insights into the literary meanings of texts over the past few decades. Building on this foundation, this study aims to enhance the understanding of literary texts by employing corpus tools to analyze the methods of characterisation in Chimamanda Ngozi Adichie’s novel, Americanah (2013). Specifically, it uses both quantitative and qualitative methods to examine the themes and characters in the novel, illuminating the author’s ideas and style. Through a corpus-based approach, this research examines the keywords and clusters that define the novel’s narrative structure, revealing the ideologies and literary techniques Adichie uses to craft characters and impart meaning about their experiences. The characterisation analysis uncovers recurring themes, rhetorical strategies, and linguistic patterns that enrich the portrayal of characters and narrative progression. The findings provide insights valuable for educational contexts, allowing students and readers to gain a thorough understanding of the text prior to delving into its more complex aspects.
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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 | 0.000 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| 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) | 0.000 | 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".