Securitisation from mortgages to sustainability: circulating techniques and the financialisation of legal knowledge
Bibliographic record
Abstract
This article explores what finance can teach us about law’s agency in global governance by tracing the global circulation of a specific technique, securitisation, from its origins in mortgage markets to its recent redeployment as a purported tool for ecological transition. It delves into legal practitioners’ literature to investigate the various legal problems that had to be addressed, and the various legal solutions that were devised, for securitisation to spread globally. Through the examination of concrete technical problems that securitisation lawyers have had to solve—concerning the constitution of special purpose vehicles, the assignment of receivables, and the construction of new asset classes—I show that what has become globalised in the field of law and finance in the past decades is not primarily a set of transnational norms or authorities, but a repertoire of legal techniques, values, and rhetoric for framing and solving legal problems: a financialised legal knowledge.
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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 | 0.003 | 0.001 |
| 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".