RE: House Bills H1794 and H1799, Acts Regarding Noncompetition Agreements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it