A Scientist and a Comedian Walk Into a Bar: Humour’s Role in Science Communication
Bibliographic record
Abstract
The art of communicating science to non-scientists (e.g., policy makers, lay-audiences) is the challenge faced by science communicators. Not only is it vital that scientific information leave the realm of academia to be put into action, but the goals of science communication increasingly include public engagement with science. Traditional tactics for public engagement have faced roadblocks in terms of being accessible and actually engaging to a broad audience. Communicators have thus identified that non-traditional techniques, such as adding humour, may make scientific information more accessible. In this MRP, I investigate the use of humour as a creative communication tool to engage the public with science in informal settings. I analyze 34 episodes of a funny science podcast, The Infinite Monkey Cage, to identify how humour is used, and by whom (e.g., scientists, non-scientists). I identify potential roles of common humour types and humour usage styles, as well as the role of each author type. These findings may serve to aid future humorous science communication endeavors, as well as to guide future research.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".