I’ll laugh, but I won’t share
Bibliographic record
Abstract
Purpose This paper aims to investigate the experience of darkness on people’s evaluation of humorous taboo-themed ads and their willingness to share these ads digitally with others. Design/methodology/approach Multiple studies are conducted to demonstrate the connection between darkness and humor. Another experiment was conducted to investigate people’s willingness to share taboo-themed ads. Findings The results demonstrate that people in dark settings (vs light) found controversial, taboo-themed ads to be more humorous. Three studies demonstrate that people in the dark (vs light) condition found taboo-themed ads to be more humorous. More importantly, despite finding taboo-themed ads to be more humorous, people in dark settings (vs light) were less inclined to share these ads on social media platforms. Practical implications When using humorous taboo-themed ads, advertisers are encouraged to show these ads in dark settings. If the physical environment is uncontrollable, marketers may still benefit by cueing consumers about darkness (e.g. through their products) or reminding them of nightly activities which may also yield similar effects. However, the cautionary tale is that, although people in the dark may enjoy these ads, they may not be willing to share it with others. Originality/value Marketers utilize taboo-themed ads to increase consumer interest. Despite its controversial content, darkness enhances people’s evaluation toward these taboo-themed ads. However, if one of the goals of advertisers is to create an ad that is amenable to sharing, developing a humorous taboo-themed ad may not be the most rewarding strategy.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".