Re-conceptualising Scientific Expertise in International Criminal Investigations:An STS Perspective
Notice bibliographique
Résumé
Contemporary societies have become reliant upon the guidance of scientific, and technological, experts whose inputs are utilized - to a hitherto unparalleled degree - by a proliferating array of complex and specialised systems,. Paradoxically, the contemporary reliance upon expert knowledge has given rise to a countervailing popular skepticism, which threatens to erode the very foundations of rational discourse, generating developmental obstacles across the panoply of natural, and social scientific domains, and creating tensions within discrete sites of technological application and epistemological uncertainty. The field of international criminal justice has come to be regarded as a particular site of contestation, the investigation and prosecution of criminal acts - at the international level - being dependent upon the collection, processing, and categorisation of a diverse body of objective evidence, drawn from multiple sources: forensic samples, documentary material, ‘open source’ data and witness statements, inter alia, are recovered, and evaluated, by a heterogeneous body of institutional actors, drawn from diverse fields and backgrounds, possessed of varying levels of expertise, and increasingly founding upon disruptive new technologies, which themselves emerge across multiple disciplinary boundaries, confounding pre-existing institutional norms and expectations. Clearly, the articulation of a coherent theoretical foundation for interdisciplinary expertise would serve all disciplines. However, that need is particularly acute within a criminal justice sector facing ethical and epistemological challenges generated by the emergence and confluence of machine learning technologies, biomedical research and the proliferating use of telecommunications data. Meanwhile, citizen participation in open source investigations has grown steadily (at least insofar as public involvement facilitates distributed data collection), offering a direct challenge to scientific and technological experts, trust in whose practices has been further eroded by the politicization of knowledge production and dissemination. If the international legal system is to maintain a robust and rational approach to the ethical, legal and social challenges engendered by machine learning, bio-medical research, and sundry emergent technologies, then its responses must be founded upon a coherent theoretical account of trans-disciplinary scientific and technological expertise: an understanding whose broader application will enable citizens and policy makers alike to answer questions related to the proper function of expertise, its efficient mobilisation, and its limits. This necessary foundational research may thereby serve as a theoretical base which subsequent elaboration may aid institutional agents in negotiating disagreements between experts, serving not merely to justify the decision-making process to the public, but to facilitate their involvement in a dialectic process of policy development. The primary objective of this paper is therefore to develop, articulate, and disseminate, a normatively coherent theoretical account of transdisciplinary expertise, as practiced in the international criminal justice sector. An account which may demonstrate the potential for STS scholarship to address this area of collective concern, resolving the ontological and epistemological tensions which have been generated by the mobilization of trans-disciplinary scientific and technological innovations, deployed across disciplinary boundaries.
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 | 0,038 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,017 | 0,013 |
| Études des sciences et des technologies | 0,007 | 0,080 |
| Communication savante | 0,027 | 0,025 |
| Science ouverte | 0,005 | 0,016 |
| Intégrité de la recherche | 0,010 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».