Guest Editors' Introduction to the Special Issue: Towards the Prevention of Genocide
Bibliographic record
Abstract
Towards the Prevention of GenocideThis issue offers an overview of recent developments in genocide prevention that are taking place in our international and intellectual landscapes.It is dedicated to analyzing the latest debates, trends and dynamics in an effort to appreciate a more systematic outlook of the field as well as to reflect upon more effective genocide prevention strategies.There is a need for linking knowledge of genocidal violence indicators and a proper course of action.There were two important moments when human collective consciousness reaffirmed its dedication to Never Again in the form of international consensus and commitment.First was when the UN General Assembly adopted the Convention on the Prevention and Punishment of the Crime of Genocide in 1948.The second took place in September 2005, when the UN General Assembly adopted its Outcome Document acknowledging the sovereign responsibility for protecting the populations from mass atrocity crimes.These are the two key documents that underlie any discussions on genocide prevention, as an expression of our collective human will trying to overcome our unfortunate propensity to willfully neglect our responsibility to prevent genocide.This issue starts with these two special contributions, highlighting how we are making progress in this regard by practicing and implementing the agreed upon norms.At the nexus of knowing genocidal risks and taking proper actions, it is important to highlight the efforts by Adama Dieng, United Nations Special Adviser on the Prevention of Genocide, and Jennifer Welsh, United Nations Special Adviser on the Responsibility to Protect, who present in this issue the conceptual overview and the practical application of the Framework of Analysis for Atrocity Crimes.The original Framework was released in 2009, and the current edition is a significant contribution to our attempts to operationalize the prevention work.While the strength of this instrument will ultimately hinge on its consistent and widespread use, both in the UN systems and national governments, the close scrutiny of the Framework signals the need for consistent investment in data gathering and verification both by the national and interactional actors.The genocide prevention can be effective only if it is predicated on sharing knowledge, tools and practices in networks of actors.The recent testimony of this orientation is the growth of the Global Action Against Mass Atrocity Crimes (GAAMAC), which concluded its second successful gathering in Manila in February 2016.It is a state-led initiative to prevent mass atrocity crimes (not only the crime of genocide), serving as a platform for exchange and dissemination of learning and good practices in order to develop national strategies and mechanisms for atrocity prevention.Another contribution comes from Ernesto Verdeja who complements the Framework by the Office of the UN Special Advisers by providing an overview of the current forecasting models that are used to predict the onset of genocide and mass killings.He surveys the increasingly sophisticated field of risk assessment and early warning practices, while evaluating how accurate they actually are, a question that is of particular interest in this issue.Prevention is deeply linked to a particular form of knowledge: politically relevant knowledge.Who is creating this knowledge?Who is making it relevant?To know accurately the early warning signs of violence in complex situations, and understand them not only early but also properly so as to employ swift and decisive measures, is a challenge Verdeja revisits.Both the risk assessment and early warning approaches are part of the prevention paradox: we can prevent only what we know and understand.His article situates discussions on the current forecasting models in terms of their applicability to actual prevention.Essential to the understanding of any risk is the use of language and especially the highly charged formulation of words aiming at or contributing to violence.The nuances of language and its use in highly hostile environments is at the core of the paper of Susan Benesch and Jonathan Leader Maynard.While distancing themselves from an oversimplified link of hate and violence by elaborating on the "dangerousness" of the speech, their contribution enhances both the theory and practice of mass atrocity risk monitoring and prevention.They combine the two existing frameworks that they have independently formulated, offering an understanding of the contextual and content-based risk factors associated with dangerous speech and ideology. Kjell Anderson and IngjerdBrakstad analyze the role of the media in shaping discourse around mass atrocities.Their discussions are underpinned by an overarching question that is deeply
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".