RE: House Bills H1794 and H1799, Acts Regarding Noncompetition Agreements
Bibliographic record
Abstract
One-third of non-competes last for more than one year; nearly 15 % extend beyond two years. Non-competes are usually requested after an offer is accepted, often on the first day at work. Less senior employees are half as likely to seek legal advice before signing a non-compete. Non-competes discourage interfirm mobility; many who change jobs take “career detours.” Non-competes act as a brake on entrepreneurial activity. Arguments that non-competes are essential for R&D investment are not supported by data. I write in support of House Bills H1794 and H1799, Acts relating to the use of employee non-competition agreements. I am currently an Assistant Professor of Technological Innovation, Entrepreneurship and Strategic Management at the MIT Sloan School of Management. Earlier in my career, I was involved with startup companies in Boston as well as Silicon Valley and hold seven patents. As both an inventor and an executive, I have experienced non-competes from both sides: I’ve asked new employees to sign them, and I’ve signed them myself. I originally became acquainted with non-compete agreements at my first job following graduate school. On my first day at work, and without prior notice, I was asked to sign a contract in which I promised not to work for any competitor for a period of two years after leaving the company. I was
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.041 | 0.013 |
| Insufficient payload (model declined to judge) | 0.256 | 0.364 |
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".