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.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".