CLÁUSULA DE NÃO CONCORRÊNCIA NOS CONTRATOS EMPRESARIAIS: UMA ANÁLISE SOBRE A INTERVENÇÃO ESTATAL DIANTE DA APARENTE COLISÃO ENTRE OS PRINCÍPIOS DA LIVRE CONCORRÊNCIA E DA AUTONOMIA PRIVADA
Bibliographic record
Abstract
This article seeks to analyze and discuss state intervention in the face of the apparent collision between the principles of private autonomy and free competition, as a direct result of the application of a very useful clause for business activity: the non-compete clause or non-compete clause . To this end, bibliographic and qualitative research were used. The non-compete clause is a true expression of the freedom that entrepreneurs have to establish, via contract, the most favorable terms for their activities. However, it appears that the agreement of the aforementioned clause by business people suffers unjustified intervention from the State, which, with the aim of protecting free competition, ends up interfering with private autonomy and preventing the free establishment of contractual terms beneficial to the enterprise and even to society. In this sense, an important discussion arises about the real impacts that non-compete clauses can cause to free competition and the need or not for state intervention with a view to modifying such clauses. Answers to such problems will only be achieved through analysis of the factors inherent to the functioning of the market, which, in turn, will prevent patent abuses in the private sphere without compromising free competition.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".