Consequences of Laughter: Reflections on Performing Comedic Self-Deprecation and Reacting to Deprecation in General
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this autoethnographic essay, the author draws on his experiences as a Chinese immigrant in Canada to reflect on how they shaped his style of comedy and how they revealed the limitations of self-deprecating humor, particularly when it is used as a response to jokes that imply discriminatory worldviews. While a self-deprecating response can steal the laugh from the person telling a derogatory joke, its outcomes are not always certain. Studies of audience responses to comedy have indicated that the presence or absence of laughter can be seen as an affirmation or rejection of the ideas embedded in the jokes, suggesting how, when self-deprecation succeeds in provoking laughter, it runs the risk of perpetuating the harmful ideas it aims to counteract. The author examines his own confrontations with racist humor alongside accounts of similar situations involving Asian comedians living in the West, such as Ryan Higa and Joe Wong, to illustrate the dilemma of self-deprecating humor as well as the potential personal and professional consequences for those who employ it.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it