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Record W2345748993 · doi:10.7202/1037249ar

Victim Pays Damages to Tortfeasor: The When and Wherefore

2016· article· en· W2345748993 on OpenAlexvenueno aff
Benjamin Shmueli, Yuval Sinai

Bibliographic record

VenueMcGill Law Journal · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsnot available
Fundersnot available
KeywordsEx-anteDamagesNuisanceLiabilityOrder (exchange)EconomicsPaymentLaw and economicsEx parteUnjust enrichmentCompensation (psychology)TortActuarial scienceLawPolitical scienceFinance

Abstract

fetched live from OpenAlex

Is there a reality in which the victim pays damages to the tortfeasor? This article analyzes Calabresi and Melamed’s liability rule for the damaging party (Rule 4), where the damaged party has the right to prevent pollution if the polluter is compensated first. Under the conventional application of this rule, the victim first collects the money and compensates the injurer, and only then is the injurer required to eliminate the nuisance (ex ante). There is no reference to a possibility of the injurer first eliminating the nuisance and only then receiving compensation (ex post). We argue that the timing of the payment should be changed when the activity causing the nuisance has social and economic value. Each version of the rule advances the aggregate welfare in some sense, but also harms it in another. The primary aim of the present article is to introduce a new model for Rule 4 that would guide legislators, regulators, and judges in deciding when to order compensation as a condition for eliminating the nuisance and when to order the injurer to remove the nuisance first and only then collect the funds. This article also introduces a comparative perspective that reveals the potential use of the ex post version of Rule 4, as manifest in sources of the Jewish legal tradition. This comparison further bolsters our proposal in favour of a division between ex ante and ex post versions of the rule. Ultimately, offering two versions for the implementation of Rule 4 would better enable the adaptation of a suitable solution according to the circumstances and thus would widen the possibilities for the rule’s use.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.017
Scholarly communication0.0080.010
Open science0.0020.002
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.027
GPT teacher head0.208
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2016
Admission routes1
Has abstractyes

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