Not all leavers are equal: How rank and destination influence enforcement of restrictive covenants
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".