Humor in Slogans: Van Helsing Effect in Second Language Learning
Bibliographic record
Abstract
The present paper further extends the studies of Eisend (2009), Takahashi and Inoue (2009), and Kohn, et al. (2011), and applies Krishnan & Chakravarti’s (2003) experiment design to examine: 1) whether humor in slogans enhances L2 learners’ memory of the promoted items in advertisements; 2) will Vampire Effect occur in humorous slogans and distract L2 learners’ focus so much that they cannot pay attention to the importance of the promoted item itself? And, 3) is gender a distinguishing feature in terms of the acceptance and sensitivity to the humor in slogans? One pretest, two experiments and one post-test were conducted in this study. In the experiments, the participants’ immediate responses to the questions and their memory of the promoted products and candidates were vital. A follow up face-to-face interview was then conducted. It was found that L2 learners’ familiarity with the promoted items was more important than the level of humor in the slogans. That is, instead of the Vampire Effect, in which the degree of funniness is so high that it overwhelms the significance of the item promoted, the Van Helsing Effect, in which L2 learners’ previous experience is more influential in the process of recognizing the slogans and the promoted items, appears.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".