Posthumous Popularity; Fathoming Vincent van Gogh through Select Biofictions
Bibliographic record
Abstract
This paper studies a few fictional representations of Vincent van Gogh in contemporary biofiction. The objective of this research is to analyse the life of a genius artist and his posthumous popularity by using the transmedia storytelling technique. Vincent Willem van Gogh, a Dutch painter who lived between 1853 and 1890, is widely regarded as one of the best exponents of post-impressionism. His eccentric life has been a perpetual obsession for creators to fictionally recreate him periodically after his disastrous end in the past hundred years. The paper captures three fictionalised biographical texts in the form of a novel, an animated movie and a documentary that transgress genre boundaries and renegotiate the relationship between historical facts and fiction. Throughout the 21st century, biographical materials, whether they are based on fact or fiction, have made significant contributions to this legend. The sources of the study pivot on three of van Gogh`s biofictions within film and literature; the novel Leaving van Gogh (2011) by Carol Wallace, the documentary Van Gogh: Painted with Words (2010) directed by Andrew Hutton and Loving Vincent (2017), a movie directed by Dorota Kobiela. The creators have taken artistic liberties by altering the stories with more engaging narratives as a way of rewriting the portrayal of the artist through fiction. Biofiction is a category of life writing that includes fictitious biographies and is typically a metafictional narrative in which a biographical subject is the protagonist or plays a significant role in the plot. Transmedia storytelling is an approach to integrating contemporary digital technology to communicate a cohesive narrative across several platforms and mediums. By utilising intermediary allusions and formal imitative techniques, van Gogh`s art, as well as the fundamental principles of artistic creation and his ubiquitous presence in contemporary times are explored.
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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 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".