Limits of legal evolution : knowledge and normativity in theories of legal change
Bibliographic record
Abstract
Over the last forty years, legal theory and policy advice have come to draw heavily from an ‘evolutionary’ jurisprudence that explains legal transformation by drawing inspiration from the theoretical successes of Darwinian natural selection. This project seeks to enrich and critique this tradition using an analytical perspective that emphasizes the material consequences of concepts and ideas. Existing theories of legal evolution depend on a positivist epistemology that strictly distinguishes the objects of social life — interests, institutions, systems — from knowledge about those objects. My dissertation explores how knowledge, and especially non-legal expertise, acts as an independent site and locus of transformation, mediating the interaction between law and social phenomena and acting as a catalyst of legal innovation. Prior work by Simon Deakin has integrated insights from systems theory to show how the interaction between law and economic institutions can only be properly understood by attending to the epistemic frame law uses to interpret economic practice. Using a case study on the impact of ‘law and finance’ literature on World Bank policy advice and, consequentially, on legal reforms adopted by many developing countries between 2000 and the present, I show that such attention to legal knowledge is inadequate. The case points, first, to the contingency of the intellectual tools used to understand legal institutions. Rather than deploying a determinate rationality, private and public actors address legal, economic, and ethical problems using a variety of paradigms: viewpoints are not determined by realities. More fundamentally, the cases suggest that successful paradigms, rather than economic or political realities alone, shape the dynamics of socio-legal change. My conclusions address some normative questions that arise when researchers in a social scientific mode are implicated in the processes they seek to document.
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 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 | 0.000 | 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".