Moving Images: WITNESS and Human Rights Advocacy (<i>Innovations Case Narrative</i>: WITNESS)
Bibliographic record
Abstract
Back in 1988 I was part of Amnesty International's "Human Rights Now!" Tour, which was to celebrate the 40th anniversary of the Universal Declaration of Human Rights.We managed to persuade Bruce Springsteen, Tracy Chapman, Youssou N'Dour, and Sting to join us, and we toured over nineteen countries.During that time I met hundreds of survivors of human rights abuses and listened to their stories of suffering and frustration.These were people who had been brutally tortured, forced to flee their homes and countries, who watched their loved ones murdered, and suffered overwhelming forces of oppression.What all of these personal accounts had in common was that the perpetrators went unpunished for their crimes.These human rights abuses were being successfully denied, ignored, and forgotten, despite many written reports.But, it was clear that in those cases where photographic film or video evidence existed, it was almost impossible for the oppressors to get away with it.The Reebok Human Rights Foundation was set up after the Human Rights Now! Tour to give awards to extraordinary young people for courage, commitment, and compassion in human rights works.At our Reebok Human Rights Foundation annual meeting, I proposed that we begin an initiative to supply human rights activists with video cameras.It was in 1992, after the videotaping of the Rodney King beating in Los Angeles, that the Foundation realized the poten-
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".