What’s in a pun? Assessing the relationship between phonological distance and perceived funniness of punning jokes
Bibliographic record
Abstract
Punning is a form of humorous wordplay based on semantic ambiguity between two phonologically similar words - the pun and the target - in a context where both meanings are more or less acceptable. While the pun is expressed explicitly, the target is invoked implicitly in the text. Previous work has attempted to quantify and compare phonological features of puns and their targets, looking at correlations with the understandability of the jokes in which they occur. Our study quantifies the phonological distance between pun and target words and assesses possible correlations with funniness ratings of the corresponding jokes. Our statistical analyses on a large dataset of puns reveal a significant negative correlation between phonological distance and perceived funniness for two of the four phonological distance measures we applied. This finding supports the hypothesis, often (implicitly) made in previous research but never verified at this scale, that lower phonological distance between a pun and its target is associated with higher funniness ratings. The parameters of our study suggest that future work should examine the semantic features of pun and target in order to create a more holistic understanding of what contributes to the perceived funniness of punning jokes.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".