Negotiated Settlements: The development of economic and legal thinking
Bibliographic record
Abstract
Negotiated settlements are a form of regulation of public utilities that is alternative or complementary to the conventional process of litigation. The Federal Power Commission pioneered the use of settlements in the early 1960s as a means of coping with an increased workload and backlog. But until recently the economic literature has had little or nothing to say about such settlements. Legal scholars have emphasized the importance of settlements in coping with the regulatory load, and in saving time and money, albeit with some concern about transparency and the treatment of non-unanimous settlements. More recently, however, they suggest that settlements better serve the needs of the parties, allow greater flexibility and innovation, and can achieve results that lie beyond traditional regulatory authority. Recent economic research has indicated the high proportion of regulatory cases dealt with by settlements in the US and Canada and confirmed that settlements are not simply a more efficient way of doing the same thing as regulation. Rather, they involve considerable innovation, notably the introduction of price caps and other incentive mechanisms that otherwise would not have been likely or even possible.
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 | 0.026 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.080 |
| Scholarly communication | 0.019 | 0.038 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".