J. L. Barnes’s <i>The Inheritance Games</i> and the Rejuvenation of Proverbs
Bibliographic record
Abstract
The present article analyzes the use of proverbs in The Inheritance Games (2020), the first book of the novel trilogy of the same name, currently enjoying great popularity and commercial success among young readers. In the first half of the book, proverbs are a central theme in the development of the action, continuing to be used frequently by various characters throughout the rest of the book, especially by Avery Grambs, the protagonist and first-person narrator. This use of proverbs in a novel targeted at young adults is remarkable inasmuch as they are a discourse device most commonly associated with older generations and traditions. Furthermore, proverbs contribute to the portrayal of various characters, illustrating their paremiological competence and the different ways in which they may be employed as established by proverb scholarship. As a result, the relevance of proverbs for the plot and their frequency of appearance challenges the widespread belief that they are old-fashioned uses of languages most frequently employed by older adults, potentially promoting an interest in them among teenagers and young adults reading the series.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".