Keywords in Wolves of the Crescent Moon: Thematic and Corpus Analysis
Bibliographic record
Abstract
Corpus methods allow for the analysis of literary texts, specifically novels, because they can objectively examine large datasets through systematic computer-based analysis. This study examined the most commonly occurring content words in the English translation of the Saudi novel Wolves of the Crescent Moon (WOTCM). This study implemented a mixed-methods approach utilizing the LancsBox software for a corpus-based quantitative analysis to identify the most frequently used content terms in WOTCM. The keywords within the context of the novel were further analyzed by comparing the terms to the semantic meanings that may be prompted by the translated title as well as the original title of the text through qualitative analysis of concordances. The findings revealed excessive usage of specific terms related to the titles, which may be attributable to the translator’s deliberate choices or the inherent aspects of the topics that could impact the interpretation of these titles. These choices highlight the importance of paratexts, which include the titles of literary works, in determining the thematic and conceptual frameworks within the novel. The study underscores the crucial significance of corpus linguistics in comprehending translation processes and the linguistic tactics employed in literary themes and sheds light on translation choices in Saudi literary works.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".