Holding Back the Tide: Genocide Prevention in Our More Violent World
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
for all the progress that was made in building barriers against genocide – and we should not shy away from acknowledging that significant progress was indeed made – we find ourselves facing a major problem. History is taking its revenge. Since the start of the ‘Arab Spring’ in early 2011, global trends in mass violence have moved consistently in the wrong direction. The number of armed conflicts have increased. Some reports suggest a six-hundred fold increase in the annual number of civilian casualties in war. Atrocity crimes are committed with increasing regularity. Perpetrators exhibit a confidence bred of impunity. Forced displacement – both internal and international – has reached levels not seen since the end of the Second World War. I want to examine this global crisis and enquire into its causes and consequences. I also want to suggest some steps that can be taken to turn the tide. I want to argue that although the struggle against genocide and mass atrocities is today confronting an acute crisis, there are grounds for thinking that determined action can hold back the tide of hate. This can be done by reinvigorating a global politics based on fundamental human rights, collective action and accountability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it