Bibliographic record
Abstract
In Canada and the United States, satire and comedy have long been staple elements in Native cultural performances, literature, and film, and humor has found some incipient discussion in critical literature. 1 In the southern part of the hemisphere, by contrast, there has been much less work on the role of humor in indigenous media. 2 Theories of humor have, of course, a long genealogy in the West, usually including contributions by Aristotle, Hobbes, Kant, Freud, Bergson, Bakhtin, and so forth. I will draw on some of these authors in my readings of indigenous videos, but am not interested in a formalistic analysis of the comic mode in film. Although formalist analyses of joke-work and humor seek to understand how mirth is constituted through a given text or action, I agree with scholars such as James English, Kristina Fagan, and Jonna Mackin that humor is more productively understood, not as an utterance, but as an event—what English calls a “comic transaction” 3 that is constituted by the contextual aspects of shared or contested social norms and popular cultural texts. That is not to affirm the commonplace notion that all humor is culturally or nationally specific4 and that an essay on indigenous media shall focus on what creates this specificity. Rather, I am interested in the sociopolitical dimension of what humor effects in a cultural politics of decolonization in which indigenous video partakes. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".