To Better Understand Vincent : A Study of The Emotional Tone of Vincent Van Gogh's Letters to His Brother Theo: 1872-1890
Bibliographic record
Abstract
Why would one study the correspondence of someone they wanted to understand better? According to Barnes (2018, p. 1), they would do this because "letters carry material traces of the emotions that motivated the writer" and also because letters "stand in for face-to-face communication with familiars". Barnes suggests that inkblots, torn pages, or hurried scrawls might provide clues as to the nature of the emotion in letters. The research described in this paper consults a different source: it looks for emotion in the words that letter-writers employ. This research analyzes the letters of the post-impressionist painter Vincent van Gogh to his brother Theo and scores them for the emotional tone of their words and for word usage patterns. The paper describes three chapters in the painter's life between 1872 and his death in 1890 (outlined in Table Vincent Van Gogh fascinates. He commands attention. He did this in intense interactions with his family (Naifeh & White Smith, 2012, chapter 1), and in his collaborations and communications with fellow artists such as Paul Gauguin (chapter 31). Vincent's admirers affirm that his letters embody both great literature (Grant, 2016, p. xi; Roskill, 2008, p. 11) and an innovative theory of art (Roskill, 2008, p. 39). Vincent commanded attention by his art, making choices for form and color that were far from usual. More than a century after his death, Vincent continues to command attention by way of the Van Gogh Museum (vangoghmuseum.nl/en), and the many critical and analytical works written about him and his art and letters (e.g., When one encounters Vincent, it is difficult to look or listen briefly and then move on. The artist's compelling oeuvre and his absorbing life, which was lived dramatically and never lacked for conflict, entangle his audience. This article studies Vincent as a letter-writer, employing techniques that limit subjectivity in the interpretation of the letters' contents. It calls on a semiobjective scoring procedure (the Dictionary of Affect in Language; The research will examine the letters, not in terms of their details or meanings but rather in terms of their emotional flavours. Pleasantness and Activation are background characteristics of the letters: they describe whole letters in terms of a single score and report on their general emotional tone. The measure of Imagery quantifies the artist's language in terms of its
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".