Bibliographic record
Abstract
The English Patient is one of the representative works of Canadian writer Mikell Ondaatje. The author tries to use archetypal criticism theory to analyze the main archetypal images in this novel, including fire, water, desert and so on. Blending multiple themes is one of the characteristics of Ondaatje's novels, and the English patient is no exception. In the novel, the author skillfully conveys his thoughts and emotions through the characterization of characters, the description of concrete things and the arrangement of plot structure. There is no absolute opposition in Ondaatje's writing. The Barbarians and the civilised, the British and the Indians, the present and the past, chaos and order, the desert and the villa, water and fire, Elmasy and Catherine versus Lao Kip and Haner, they overlap and diverge. Almasy's desperate attempts to erase his nationality, Kip's desire to give up his cultural identity and follow the west, two couples struggling in different situations, and so on, these people's efforts in the face of reality has always seemed so pale. These images seem to be fragmentary, but they are related to each other. They bring together the theme of the novel and convey the author's deep understanding of the disasters that war brings to mankind, and the desire and belief of multi-cultural co-existence.
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 | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".