The Human Rights Film Network: festival resilience in the time of Covid-19
Bibliographic record
Abstract
The year 2020 was marked by Covid-19 health emergencies disrupting lives and societies on a global scale. No country or sector of our everyday life has been left unscathed by these unprecedented levels of sudden change and urgent adaptation that we all have experienced in different ways. Creative sectors also needed to quickly adapt and cope, while the importance of culture and the arts have once more emerged as key elements for resistance and nurturing in a time of crisis. Film festivals across the world and the film industry have also been impacted – some events were cancelled such as Cannes, Tribeca, Telluride, SXSW, Mumbai, and Edinburgh Film Festival; many opted for online versions such as Sheffield, Sarajevo, Thessaloniki, New York, HotDocs, and CPH:DOX. From September onwards a few have started to run again physically – Venice, London, Toronto, Pingyao, Jenjou, Busan – at times combining some blended elements of online streaming to reach out to global audiences missing the usual international festival calendar, especially film industry delegates. \n \nThis article focuses on how human rights festivals across the world have creatively and resourcefully resisted and adapted to Covid-19. It analyses the impact of this crisis on festival organisations within their contexts and on the global outreach that was possible thanks to the connections of the Human Rights Film Network (HRFN). The findings in this article are based on a survey that collected testimonies of how HRFN members responded to Covid-19 between April and November 2020 as well as from a global virtual meeting that was organised in July 2020 as an opportunity to ‘bring us closer together at this time of difficulty and uncertainty, allowing all members to meet, share ideas for online activities to work more closely and collaboratively in the future’.[1] As producers of human rights film festivals who are members of the network and who attended these live meetings, we experienced firsthand the conversations that emerged, the reactions from fellow festival producers, as well as the importance of having such spaces to share concerns and advice. These reports help us understand how human rights festivals across the world have experienced this crisis within their geographic contexts and to ask questions such as: What was the impact of such emergencies on festival organisations in different contexts? What role did creativity, resistance, community solidarity and social networks play in the different local contexts? And finally, what was the role of the HRFN and its global outreach, in terms of connections, solidarity, and sharing of knowledge?
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".