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Record W4404581579 · doi:10.1016/j.jbvi.2024.e00505

Not all leavers are equal: How rank and destination influence enforcement of restrictive covenants

2024· article· en· W4404581579 on OpenAlexafffundabout
Sepideh Yeganegi, André O. Laplume, Bradley Bernard

Bibliographic record

VenueJournal of Business Venturing Insights · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsTed Rogers Centre for Heart ResearchWilfrid Laurier University
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsCovenantEnforcementBusinessRank (graph theory)Labour economicsDemographic economicsMathematicsEconomicsPolitical scienceLawCombinatorics

Abstract

fetched live from OpenAlex

Restrictive covenants like non-competes, non-solicitations, and non-disclosures may pose barriers to spinout ventures and mobility to competitors. However, we know little about the enforceability of these agreements despite their widespread use and associated chilling effects. Examining 332 Canadian court decisions, we find a higher rate of enforcement in cases involving high rank leavers (i.e., managers and owners) versus low rank leavers (regular employees and contractors) especially those who form spinout ventures. Our key insight is that enforcement rates differ significantly across different types of leavers. Low rank leavers and their previous employers may overestimate the potential for enforcement, creating chilling effects (i.e., where employees think they are more restricted by their employment agreements than they really are) that can deter employee mobility and entrepreneurship. • Restrictive covenant enforcement rates differ significantly across types of leavers. • High rank leavers that do spinouts are most vulnerable to enforcement. • Low rank leavers may overestimate restrictive covenant enforcement, leading to chilling effects.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.225
Teacher spread0.197 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2024
Admission routes3
Has abstractyes

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