Guest Editors' Introduction to the Special Issue: Towards the Prevention of Genocide
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,008 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».