Bibliographic record
Abstract
In my chapter, I will give an overview of the many means and the variety of genres of protest expression. The characteristics of humour make it an instrument of protest against the system in the broad sense that we use as a starting point for our entire study. Humour challenges the established structures of meaning and patterns of communication. This allows it to fulfil its main task: to test the system for its strength. In culture, this function is necessary for self-correction and development; it also provides a creative insight, a necessary element of the creative process. However, in the conditions of containment of culture, the attempts to “freeze” it in favour of the narrow interests of individuals or social groups, the restriction of creativity leads to humour becoming a special creative current in culture that includes representatives of various social groups without restriction. Perhaps against the backdrop of general cultural stagnation, humour only becomes more visible. The chapter explains in detail the theoretical foundations of this approach to interpreting humour and uses the example of protest activities in Russian culture of the 2010s and early 2020s to show how humour becomes an instrument of protest. The chapter introduces diverse aspects of protest/humour, such as visual forms, narratives, actionism and others.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
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".