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Record W7123356684 · doi:10.21776/rechtjiva.v2n3.7

Pengenaan Biaya Tambahan Pada Transaksi Jual Beli Di E-commerce dan Marketplace

2025· article· W7123356684 on OpenAlexaboutno aff
Jauza Afraa Fadhillah, Djumikasih Djumikasih

Bibliographic record

VenueRechtjiva · 2025
Typearticle
Language
FieldSocial Sciences
TopicLegal and Policy Analysis in Indonesia
Canadian institutionsnot available
Fundersnot available
KeywordsTransparency (behavior)CLARITYContext (archaeology)Consistency (knowledge bases)NormativeIdeal (ethics)

Abstract

fetched live from OpenAlex

In sales transactions through e-commerce platforms, this issue is examined from the perspective of consumer protection law in Indonesia. This issue is raised due to the absence of norms regarding price transparency and maximum limits on additional fees imposed by businesses, which has an impact on legal uncertainty for consumers. This study formulates two main problems, namely, the weakness of regulations on additional fees outside the base price in the Consumer Protection Law, and ideal alternative regulations for such practices in the context of e-commerce in Indonesia. The research method used is a legal normative approach combining legislative, conceptual, and comparative methodologies. The findings indicate that Indonesia’s legal framework has not yet specifically addressed the imposition of additional fees, despite such practices already occurring across various platforms. Article 10 of the UUPK on regulates base prices and discounts, without covering additional fees. Comparative studies with Canada and Australia show that both countries have regulated price transparency through regulations that require clarity of information and consistency between advertised prices and charged prices. Based on these findings, and ideal legal framework adapted from best practices in both countries is proposed.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.004
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.022
GPT teacher head0.328
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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