Legal Protection of Intellectual Property Rights for Micro, Small and Medium Enterprises (MSMEs) Products in Kendari City
Bibliographic record
Abstract
The purpose of this research is to find out the legal protection of Intellectual Property rights for MSME products in Kendari City and the efforts of the Kendari City government in order to provide legal protection of intellectual property rights over MSMEs products. This research uses normative juridical research methods, namely legal research conducted by examining library materials or secondary data as basic materials to be researched by conducting a search of regulations and literature related to the problems studied. This research emphasizes and used the approaches such as statute approach and conceptual approach. The results showed that: 1) The legal protection of intellectual property rights to MSMEs products in Kendari City included brand protection, copyright, industrial design, trade secrets, and patents. Brand Protection is the most widely used choice by MSMEs in Kendari City to create a strategic bargaining position on a national and international scale. This is in accordance with the data of IPR registration applications in Kendari City which is dominated by registration of brand registration applications. Types of IPR protection other than brands can also be utilized by MSMEs by looking at the advantages and disadvantages of IPR protection for use in business activities carried out, and 2) Efforts of the Kendari City government to protect and empower Small and Medium Micro Enterprises in Kendari City include: a) Increasing human resources Capacity through technical guidance, b) Providing training and socialization to MSME actors regarding IPR registration procedures, c) The IPR registration fee of MSME products is cheaper, d) Give an Intensive financing to IPR registration for MSME products.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".