The Evolution of the Idiolect over the Lifetime: A Quantitative and Qualitative Study of French 19th Century Literature
Bibliographic record
Abstract
The way in which authors express themselves is unique but changes over their lifetime. However, quantitative studies of this idiolectal evolution are rare. Using the Corpus for Idiolectal Research (CIDRE) that contains the dated works of 11 prolific 19th century French fiction writers, we propose new methods to identify, quantify and describe the grammatical-stylistic changes that take place using lexico-morphosyntactic patterns, also called motifs. To examine the strength of the chronological signal of change, we developed a method to calculate if a distance matrix of literary works contains a stronger chronological signal than expected by chance. Ten out of 11 corpora showed a higher than chance chronological signal, leading us to conclude that the evolution of the idiolect is in a mathematical sense monotonic, supporting the rectilinearity hypothesis previously put forward in the stylometric literature. The rectilinear property of the evolution of the idiolect found for most authors in CIDRE subsequently enabled us to propose a machine learning task: predicting the year in which a work was written. For the majority of the authors in our corpus, the accuracy and the amount of variance that is explained by the model were high and we discuss why the technique might fail for others. After applying a feature selection algorithm, we examined the most important features, i.e. the motifs that have the greatest influence on idiolectal evolution. We find that some of those features are stylistic and have been previously identified in qualitative literature studies. We report some remarkable stylistic constructions revealed by our algorithm to illustrate which kind of stylistic patterns can be extracted using our method.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".