Bibliographic record
Abstract
In recent years, the phenomenon of cancel culture has been gaining traction on social media as well as in real life. Considering the formation of this phenomenon, the authors of the article analyse its emergence and the way it spreads in society. The authors of the article emphasise the fact that cancel culture arose as a means of drawing attention to the actions of individual politicians, committed earlier, but unacceptable in modern society. Gradually, cancel culture has extended from the tool of condemnation on social media to its use in political struggle. The authors of the article analyse cases of politicization of cancel culture in the USA, Canada and Ukraine. Based on the analysis, the authors come to the conclusion that a common feature for all countries is the attempt to reconsider historical events through the lens of cancellation. The attitude towards cancel culture in modern societies is rather ambi-guous. The consequence of this is the appearance of supporters and critics of this phenomenon. Extending cancel culture to the political struggle carries certain risks associated with the need for excessive control over freedom of thought and speech. ‘Cancellation’ is used not only as a tool for manifesting a position on social media, but also becomes a tool in manipulative campaigns and information wars. Meanwhile, conducted studies show that the very appearance of such a phenomenon leads to the fact that representatives of certain groups are not ready to defend their views, if such views are not widely shared by society. That is why the phenomenon of cancel culture needs further detailed study and understanding.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".