Bibliographic record
Abstract
ABSTRACT This article claims that the principles of dialogic discourse are applicable to both verbal and iconic languages, because they share certain functions, such as the all-important metalinguistic one. The article studies in detail, from a Bakhtinian perspective, a series of six paintings created by the 17 th century Dutch artist, Nicolaes Maes (1634–93). Each painting depicts different poses and gestures of an eavesdropper, in such a manner that the Bakhtinian analyst-viewer is obliged to see how painting of this curious sort combines surprising verbal and visual languages. Maes’ eavesdropper paintings concern curiosity, bringing together characters who might have preferred to remain independent of one another. The paintings deploy gestural, bodily, linguistic, and colour codes and make the visual material work creatively, allowing each language to take advantage of the expressive advantages of other languages. Several vantage points combine to show that the expressive capabilities of any given language are necessarily poorer when they rely on a single medium. A Bakhtinian perspective can shed new light on the paintings of Nicolaes Maes, while the analysis illumines new semantic possibilities in the thought of Bakhtin. KEYWORDS: Painting; Iconic Languages; Verbal Language; Dialogic Discourse; Curiosity
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".