Bibliographic record
Abstract
Abstract We live in societies in which an ever-growing array of activities in a range of fields are assessed, controlled and governed according to quantitative techniques. This paper focuses on processes grounded on quantitative tools (e.g. scores, indicators, rankings, algorithms) that are used in order to measure the performance of processes, people or organizations. Such processes are referred to in this paper as ‘legal metrics’. We explore how different legal systems and sectors rely on legal metrics, and develop a twofold argument. On the one hand, we argue that performance-based quantification often qualifies as a form of regulatory intervention. On the other hand, we contend that performance-based measures are always adopted and applied in legal contexts that react differently to the quantification turn. Both sides of this picture have largely been overlooked by comparative lawyers, even though they imply looking for law in extraordinary places and appraising a variety of legal manifestations in different settings. Understanding which forms of quantitative measures are widespread, in which sectors and regions, made by whom and producing what regulatory effects, is a research task that could (and in our opinion should) be of interest for all comparative law scholars.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".