Creativity in the Context of Risk: the Impact of Digital Transnational Repression on Artistic Expression
Bibliographic record
Abstract
In the Fall of 2022, the arrest and subsequent death of 22-year-old Mahsa Amini in Tehran sparked mass protests demanding greater freedoms and an end to repressive laws for women in Iran. Artists played a pivotal role in what became known as the “Women, Life, Freedom” movement. In Iranian diasporic communities around the world, artists amplified the voices of protestors through visual art, film, music, poetry, and performance, drawing global attention to the movement's demands for justice and freedom. In doing so, many of these artists took significant risks. The Iranian regime is known to reach across borders to stifle opposition using a broad range of techniques – from kidnapping, detention, and killing through to online tactics of harassment, surveillance, and threats, including threats to family members. And while more severe situations attract media and government attention, it is the “everyday” tactics, amounting to a “constant barrage of harassment, intimidation, and surveillance,” that leave many artists vulnerable, without support, and can lead to self-censorship (Schenkkan and Linzer, 2021, p. 37). These new tools of digital transnational repression are practical from the perspective of authoritarian states whose interest is to silence dissent. And artists are particularly vulnerable (Whyatt, 2023; United Nations Educational, Scientific and Cultural Organization, 2023). This paper examines the recent tactics of digital transnational repression and their impact on artists, outlining the levers, tools, and resources that organizations and institutions supporting artists need to engage in order to protect the freedom of artistic expression, which is fundamental to democratic societies.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".