Re-conceptualising Scientific Expertise in International Criminal Investigations:An STS Perspective
Bibliographic record
Abstract
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.
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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 | 0.038 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.007 | 0.080 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".