Perlindungan Hukum Terhadap Konsumen yang Dirugikan Atas Kelalaian Pelaku Usaha Cuci Sepatu di Denpasar Timur
Bibliographic record
Abstract
Public interest in shoes is a special need, shoes are in great demand by millennials because they are useful for protecting feet. The habit of collecting these shoes arises from the desire of humans to keep their goods so they don't get damaged quickly and stay clean for a Long time. However, there are still cases of damage to shoes that cause Losses to consumers and there is no responsibility from the shoe washing service. The research method used is the type of empirical Law, empirical legal research aims to find facts, and conduct interviews. There are internal and external factors that cause damage to consumer shoes by business actors. So that responsibility for losses cannot be carried out unilaterally between business actors and consumers because there are Legal remedies that can be taken if they are harmed. Lack of knowledge and understanding fromconsumers, makes the position of consumers lower than business actors. So with the existence of Law Number 8 of 1999 concerning Consumer Protection, both parties need to know so that their rights and obligations are not violated.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".