THE CHARACTERIZATION OF HASAN MINHAJ’S HUMOR LANGUAGE IN THE DAILY SHOW WITH TREVOR NOAH’S SHOW THE BEST OF HASAN MINHAJ – MUSLIM BAN, WOMEN’S SOCCER & CANADA
Bibliographic record
Abstract
Humor can be presented as a method to communicate with others. The humor language used to communicate with others has several types, which also have their own styles, characteristics, and uniqueness, to deliver the ideas. As an occupation that uses humor to communicate with people, comedians want to deliver the topic or issue which happens in daily social life with their own style. The focus of this research is to identify the types of humor language used by Hasan Minhaj in the variety show The Daily Show with Trevor Noah. The researcher applies the combinations of two theories which are the semantic script-switch trigger concept by Raskin as the main theory and the general theory of verbal humor by Attardo as the supporting theory. Moreover, this research uses a descriptive qualitative method. The data of this research comes from the variety show The Daily Show with Trevor Noah’s script entitled The Best of Hasan Minhaj – Muslim Ban, Women’s Soccer & Canada. In this research, the researcher found 2 data of regular ambiguity, 1 data of figurative ambiguity, 2 data of situational ambiguity, 4 data of contradiction trigger, and 1 data of dichotomizing trigger. Based on the findings, it can be stated that the most used type of humor language used by Hasan in The Daily Show with Trevor Noah is the contradiction trigger. After that, there is situational ambiguity and regular ambiguity, which have 2 data for each type. Then, figurative ambiguity and dichotomizing trigger with each of the types has 1 data.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".