dc.contributor.author | Scheinert Idodo, L | |
dc.date.accessioned | 2024-10-07T11:58:18Z | |
dc.date.issued | 2024-10-07 | |
dc.date.updated | 2024-10-06T10:08:48Z | |
dc.description.abstract | Evaluating judicial training at the system level has been a long-standing challenge in judicial training evaluation. To tackle it, this thesis devises a consolidated framework for evaluation at all levels: the approach provides accountability and feedback for training improvement while addressing potential concerns about judicial independence, which have often acted as a barrier or “bullet-proof vest” against evaluation. The thesis starts by offering a clear definition of judicial training, conceptualising it as non-formal learning; this acknowledges the absence of fixed syllabi or external accreditation and implies independence-maintaining methods for system-level evaluation. From that, the study develops a context-specific, purpose-driven, mixed methods framework of training effectiveness evaluation at all levels. Finally, it implements the framework via a case study of the understudied United Kingdom’s First-tier Tribunal Immigration and Asylum Chamber (FtTIAC), an administrative tribunal deciding complex immigration and refugee status cases in often controversial political contexts. The approach proposes a novel, hypothesis-led application of methods from the Artificial Intelligence and Law/ judicial analytics fields to support system-level evaluation based on judicial decisions analysis and benefits from rare access to judges, Judicial College staff, and training courses. Findings demonstrate 1) a mismatch between levels of current training aims formulation and evaluation, 2) mixed outcomes (judges learn and they do not, they change their practice and they do not), and 3) persisting overturn rates and error types for the overall small percentage of appealed FtTIAC decisions. The study makes three key empirically based recommendations: training could 1) better leverage social interactions to support learning, 2) differentiate settings, methods, and contents for more targeted provision, and 3) draw on the full range of activities and materials to effect learning and practice change. While reflecting critically on the ability to evaluate training at the system level, the proposed approach, which should be tested in other contexts, turns the “bullet-proof vest” into a tool kit – after all, it is effective training that “bullet-proofs” judges for their independent adjudication task. | en_GB |
dc.description.sponsorship | Economic and Social Research Council (ESRC) | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/137626 | |
dc.language.iso | en | en_GB |
dc.publisher | University of Exeter | en_GB |
dc.rights.embargoreason | This thesis is embargoed until 07/Apr/2026 as the author plans to publish papers using material that is substantially drawn from the thesis. | en_GB |
dc.subject | judicial training evaluation | en_GB |
dc.subject | machine learning | en_GB |
dc.subject | natural language processing | en_GB |
dc.subject | judicial decision classification | en_GB |
dc.subject | mixed methods research | en_GB |
dc.subject | Kirkpatrick evaluation model | en_GB |
dc.subject | First-tier Tribunal Immigration and Asylum Chamber | en_GB |
dc.subject | impact evaluation | en_GB |
dc.subject | judicial analytics | en_GB |
dc.title | The ‘bullet-proof vest’: remits and limits of judicial training and its evaluation. An exploration of the United Kingdom’s First-tier Tribunal Immigration and Asylum Chamber | en_GB |
dc.type | Thesis or dissertation | en_GB |
dc.date.available | 2024-10-07T11:58:18Z | |
dc.contributor.advisor | Gill, Nick | |
dc.contributor.advisor | Tonkin, Emma | |
dc.contributor.advisor | Beduschi, Ana | |
dc.publisher.department | Geography | |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dc.type.degreetitle | Doctor of Philosophy in Advanced Quantitative Methods in Social Sciences | |
dc.type.qualificationlevel | Doctoral | |
dc.type.qualificationname | Doctoral Thesis | |
rioxxterms.version | NA | en_GB |
rioxxterms.licenseref.startdate | 2024-10-07 | |
rioxxterms.type | Thesis | en_GB |