Legal Effective of Putting "Business as Usual" Clause in Agreements
Bibliographic record
Abstract
The development of business in Indonesia accompanies legal needs in the community, especially when triggered by the pandemic period of 2020, breakthroughs of business people to anticipate obstacles have made innovative clauses to be put in the agreement. The birth of the “business as usual” clause needs to be observed in this paper to study the legal impacts that may arise from its positive and negative aspects. This paper aims to analyze the impact of the agreement on the placement of a business as a usual clause on force majeure conditions and to examine the relevance of responsibility as good faith, its validity is limited so that risks can be minimized. This research paper use a random sampling method, normative-empirical legal research, socio-legal research tool types, data software through legal observations, analysis descriptions, modern or traditional agreement law reviews, and primary and secondary data collection. The results of this paper's research manifest the dispute resolution of the parties due to the agreement if it is not proven in good faith. This paper provides solutions and studies of the agreement law of business actors in an effective manner of responsibility and justice.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".