Bibliographic record
Abstract
Humor is considered a common element of human interaction. It is sometimes used to enhance the utterances so as to make them more comfortable. That is why it has been given a considerable attention by many scholars from different fields of knowledge such as linguistics, psychology and sociology. In linguistics, many scholars have tried to define humor and to show its functions and the factors that enable the humor act to be adequate and interesting. This led many theories and approaches to be formulated in order to study humor from different perspectives among which the incongruity theory by Kant (1790) and the relief theory by Moreal (1983). In addition, the non-observance of Grice’s conversational maxims (1975) can also be used to create humor. Accordingly, this study aims at analyzing humor as a strategic means by which participants achieve their goals in interpersonal interaction, in particular in TV Shows, namely, Oprah Winfrey Show and Piers Morgan Show. The study also aims at investigating the way by which participants shift the topic of interaction whenever they try to avoid a certain topic by shifting to a humorous style. It is concluded that there are many factors that affect humor in TV talk shows including the personality of the host and his/her interviewees, the topic of interaction and the way through which a character deals with a certain topic.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".