Bibliographic record
Abstract
Toni Morrison is the first black woman winning Nobel Prize for literature. She always focuses on the living conditions and spiritual world of black people as well as the mental process and unyielding struggle under such conditions. Her fifth novel, Beloved has won wide recognition since its publication. With superb writing techniques and full emotion, she shapes the image of the black characters vividly. One of the salient characteristics is the application of metaphorical language. As the language tool and main thread of the novel, metaphors not only strengthen the artistic effects of the work, but also contribute significantly to the exploration of its deep themes and dense implications. Based on Conceptual Metaphor Theory (CMT) put forward by Lakoff & Johnson and Conceptual Blending Theory (CBT) by Fauconnier & Turner, the thesis aims to analyze the metaphors of the novel. Traditional research of metaphors focuses on the rhetoric function and artistic effect, while CMT and CBT do the job from the level of the cognitive function of metaphors, thus providing us with better semantic and contextual understanding. The article is composed of five parts. The first part gives a brief introduction to Toni Morrison and her works. The second part introduces the development and significance of CMT and CBT. In the third part, the author tries to analyze the metaphorical meanings of the governing image of water. Closely interwoven with each other, these metaphors reveal the evil of slavery and the self-redemption of black spirit as well as forming a huge network. Therefore, the fifth part summarizes the case studies of the previous parts and analyzes the systematicity of metaphors to unveil the “huge iceberg” behind it, thus demonstrating the writing intentions and keen insight of the writer in hope that the thesis will offer a new perspective to understand and appreciate the novel Beloved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.003 | 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".