ANALISIS REPRESENTASI GENDER ANTARA SENIMAN PEREMPUAN DAN LAKI-LAKI DALAM KARYA SENI NFT INDONESIA
Bibliographic record
Abstract
Penelitian ini berusaha meninjau karya-karya seniman NFT Indonesia dalam merepresentasikan ekspresi gender dalam karya seni mereka untuk mengidentifikasi bagaimana gender direpresentasikan dan disampaikan dalam budaya visual terkini yakni seni NFT. Melalui peninjauan kritis berangkat dari pertanyaan dasar tentang apakah masih terjadi perbedaan cara pandang dalam merepresentasikan gender di dunia kini, penelitian ini juga ditujukan untuk mengidentifikasi adanya dominasi maskulinitas dalam desentralisasi pada sistem blockchain. Penelitian ini dapat membantu mengungkap kesenjangan dalam representasi gender dalam karya seni NFT untuk melihat produk-produk ketimpangan dan bias gender dalam seni NFT. Metode dalam penelitian ini melibatkan analisis sistematis terhadap nilai visual karya seni NFT yang divalidasi melalui crosscheck wawancara dengan narasumber. Secara keseluruhan, penelitian tentang pemahaman komparatif dan representasi gender dalam seni NFT memiliki nilai besar dalam mempromosikan inklusi, meruntuhkan stereotip, dan memperluas pandangan kita tentang gender dalam seni. Penelitian ini dapat berkontribusi pada pemahaman yang lebih baik tentang isu-isu gender dan berkontribusi pada diskusi dan perubahan positif di dunia seni.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 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".