The Corpus-based Multidimensional Analysis of Hemingway’s The Torrents of Spring
Bibliographic record
Abstract
This study employs a corpus-based multidimensional analysis, as proposed by Biber, to investigate the linguistic features of Ernest Hemingway’s first novel, The Torrents of Spring, compared to his other works. Through a corpus analysis of 21,709 tokens from The Torrents of Spring as the study corpus and 725,674 tokens from eight of his later novels as the reference corpus, the study sheds light on Hemingway's unique writing style by examining his linguistic preferences. The results show notable variations in 20 linguistic characteristics, including The Torrents of Spring's greater use of nouns, longer average word lengths, greater prepositional frequency, and fewer causal subordinations. These findings imply that Hemingway's approach in this early work is distinguished by emphasizing informative discourse, incorporating significant substance without prioritizing interaction.Furthermore, the study identifies six textual dimensions based on Biber's framework, highlighting that while some dimensions show similarities across Hemingway’s works, dimensions such as "Involved vs. Informational Production" and "Online Informational Elaboration" exhibit striking contrasts. The analysis demonstrates that Hemingway's first novel is marked by a significant emphasis on precise lexical choices and structured information delivery, in contrast to his later writings' more interactive and emotionally nuanced style.By offering factual proof of Hemingway's linguistic inclinations, this study advances our knowledge of his changing writing style. It also fills a vacuum in the literature concerning the particular linguistic elements of The Torrents of Spring, providing new light on the intricacies of Hemingway's storytelling style. The findings highlight the value of corpus-based approaches in literary analysis and help readers understand the complex relationship between language choices and story structure in Hemingway's body of work.
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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 | 0.000 | 0.001 |
| 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".