Revisiting John Stuart Mill’s <em>The Subjection of Women</em>: A Computer-Assisted Stylometric Analysis
Bibliographic record
Abstract
According to John Stuart Mill’s Autobiography, his mature work should be thought of as “the product not of one intellect and conscience but of three” (J. S. Mill [1873] 1981, 265). He claimed that The Subjection of Women (1869) was co-authored by himself, Harriet Taylor Mill, and Helen Taylor. Most of J. S. Mill’s readers have been largely unconvinced both by his claims of co-authorship and by his encomiums of his co-authors. Rather than strengthening the claims of a common “fund of thought,” collaboration, and co-authorship, his testimony to their abilities undermined them. Those who are most reluctant to take these claims at face value reject the idea that not only did Harriet Mill have an active, pervasive, and everlasting part in John Stuart Mill’s writings, but also that she was the originator of some of his most characteristic ideas. Others, however, readily admit her influence and her originality. Unlike her mother, Helen Taylor has never actually gotten any consideration as her stepfather’s co-author. Given the challenges of assessing authorship for this text through traditional methods, we apply computational stylometric analysis. Should we accept a key tenet of stylometric studies, that an author’s mind engrafts itself onto the text, then we might be able to test J. S. Mill’s claims of co-authorship. This paper presents the state of the question and the results of a supervised machine learning-based authorship identification analysis of the Subjection. We train three classifiers (SVM, K-NN, DT) on a dataset of essays by all three potential authors. These models are then used to attribute segments of the Subjection to individual authors. The most effective models attribute the majority of the text to John Stuart Mill, though stylistic traces suggest contributions from Harriet Mill and, to a lesser extent, Helen Taylor. This is a particularly difficult authorship identification issue to address. Selon Autobiography de John Stuart Mill, son œuvre mûre doit être considérée comme « the product not of one intellect and conscience but of three » (J. S. Mill [1873] 1981, 265). Il affirmait que The Subjection of Women (1869) avait été coécrit par lui-même, Harriet Taylor Mill et Helen Taylor. La plupart des lecteurs de J. S. Mill sont restés largement sceptiques, tant face à ses affirmations de coécriture que face à ses éloges de ses collaboratrices. Au lieu de renforcer les affirmations d’un « fonds commun de pensée », de collaboration et de co-auteurialité, ses témoignages sur leurs compétences les ont affaiblies. Ceux qui rejettent le plus nettement ces affirmations refusent l’idée que Harriet Mill ait eu un rôle actif, constant et durable dans les écrits de John Stuart Mill, et encore moins qu’elle ait été à l’origine de certaines de ses idées les plus marquantes. D’autres, en revanche, reconnaissent volontiers son influence et son originalité. Contrairement à sa mère, Helen Taylor n’a jamais été sérieusement considérée comme coautrice de son beau-père. Étant donné la difficulté d’évaluer l’auteur du texte par des méthodes traditionnelles, nous appliquons une analyse stylométrique computationnelle. Si l’on admet un principe fondamental des études stylométriques — qu’un texte porte l’empreinte cognitive de son auteur — alors il est possible de tester les affirmations de J. S. Mill concernant la coécriture. Cet article expose l’état de la question et les résultats d’une analyse d’attribution d’auteur fondée sur l’apprentissage supervisé appliquée à The Subjection. Nous entraînons trois classificateurs (SVM, K-NN, DT) sur un corpus d’essais des trois auteurs possibles. Ces modèles sont ensuite utilisés pour attribuer des segments du texte à chaque auteur. Les modèles les plus performants attribuent la majorité du texte à John Stuart Mill, bien que des traces stylistiques indiquent des contributions de Harriet Mill et, dans une moindre mesure, de Helen Taylor. Ce cas pose un problème d’attribution particulièrement difficile.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".