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Record W1512878900

Negotiated Settlements: The development of economic and legal thinking

2006· preprint· en· W1512878900 on OpenAlexaboutno aff
Joseph A. Doucet, Stephen Littlechild

Bibliographic record

VenueRePEc: Research Papers in Economics · 2006
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsnot available
Fundersnot available
KeywordsHuman settlementIncentiveTransparency (behavior)CommissionBusinessCoping (psychology)Law and economicsEconomicsPolitical scienceMarket economyLawEngineeringFinancePsychology
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.280
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2006
Admission routes1
Has abstractyes

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