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Record W7014287062

Personal Liability of Directors and Officers in Tort: Searching for Coherence and Accountability

2019· report· en· W7014287062 on OpenAlexaffabout

Bibliographic record

VenueDeep Blue (University of Michigan) · 2019
Typereport
Languageen
Field
Topic
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTortAccountabilityLiabilityCompensation (psychology)Economic JusticeCorporate law
DOInot available

Abstract

fetched live from OpenAlex

The 21st century has been marred by corporate scandal after scandal, including financial fraud, pyramid schemes, international bribery, and decades of sexual harassment. This raises an important question regarding the role of corporate and tort law in controlling the behavior of corporate executives more broadly. It is clear that directors and officers should not be overexposed to tortious liability – doing so would ultimately make them insurers of the firm’s obligations. Yet underexposure creates its own set of problems, including a lack of accountability when directors and officers are not required by law to conduct themselves reasonably. The purpose of this Article is to address how U.S. state courts attribute personal liability in tort to directors and officers in actions by non-shareholder third parties. It does so, in part, by relying on Canadian law as a comparator as well as on Professor Lewis Checchia's admonishment that the law must not "reward unreasonable and unethical conduct" nor "deny recovery to injured third parties with valid legal claims." The Article concludes that, contrary to the law in certain U.S. jurisdictions, directors and officers liability should be assessed according to the ordinary principles of tort law. Defenses based on the special status of directors and officers are objectionable because they degrade corporate culture, generate moral hazard, and deny justice to the otherwise worthy plaintiff.

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)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.083
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.010
Scholarly communication0.0110.007
Open science0.0010.006
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.029
GPT teacher head0.268
Teacher spread0.238 · 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 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

Citations0
Published2019
Admission routes2
Has abstractyes

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