Bullshit makes the art grow profounder
Bibliographic record
Abstract
Abstract Across four studies participants ( N = 818) rated the profoundness of abstract art images accompanied with varying categories of titles, including: pseudo-profound bullshit titles (e.g., The Deaf Echo ), mundane titles (e.g., Canvas 8 ), and no titles. Randomly generated pseudo-profound bullshit titles increased the perceived profoundness of computer-generated abstract art, compared to when no titles were present (Study 1). Mundane titles did not enhance the perception of profoundness, indicating that pseudo-profound bullshit titles specifically (as opposed to titles in general) enhance the perceived profoundness of abstract art (Study 2). Furthermore, these effects generalize to artist-created abstract art (Study 3). Finally, we report a large correlation between profoundness ratings for pseudo-profound bullshit and “International Art English” statements (Study 4), a mode and style of communication commonly employed by artists to discuss their work. This correlation suggests that these two independently developed communicative modes share underlying cognitive mechanisms in their interpretations. We discuss the potential for these results to be integrated into a larger, new theoretical framework of bullshit as a low-cost strategy for gaining advantages in prestige awarding domains.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".