Bibliographic record
Abstract
This book confronts the difficulties raised by cross-border evidence in order to propose a new understanding of justice as approximative. Can there be any common sense of justice across the European Union (EU)? This book takes up this question which is raised directly in cases where the understanding of cross-border evidence encounters national and linguistic differences. The interpretive challenges this introduces impact the possibility of justice in a way that, the book argues, cannot be resolved with recourse to some ideal of harmonization that would simply flatten these differences. Rather, these cases – taken here from Sweden and France – raise a practical, but also a theoretical, question about how justice can be done. In response, the book draws on contemporary theorizations of justice to argue against a common sense of justice in the sense of what would be a correct legal judgment. In its place, the book elaborates an idea of justice that maintains, rather than collapsing, the differences presented in cases of cross-border evidence; and which therefore aims to be ‘approximative,’ or ‘good enough,’ rather than simply correct. This book will be of interest to readers in legal theory, socio-legal studies, comparative law and European Union law.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".