Analisis Semiotika pada Poster Manner Oleh Perusahaan \nTokyo Metro Tahun 2020
Bibliographic record
Abstract
Penelitian ini mengenai tanda lingual dan nonlingual yang terdapat di dalam \nposter manner perusahaan Tokyo Metro yang terbit pada tahun 2020. Penelitian ini \nmerupakan kajian semiotik. Metode yang digunakan dalam penelitian ini ialah \nmetode kualitatif yang menghasilkan data secara deskriptif. Data dianalisis dengan \nmenggunakan metode padan ortografis dan menggunakan teknik pilah unsur \npenentu. Peneliti menggunakan teori Roland Barthes untuk menganalisis data. Hasil \npenelitian ini menunjukkan bahwa poster manner memiliki tema yang berbeda \nsetiap tahunnya. Pada tahun 2020, Tokyo Metro menerbitkan poster ilustratif \ndengan tema folklor Jepang. Ilustrator menghubungkan tata krama di kereta api \ndengan karakter dalam cerita folklor. Pada poster, terdapat tanda lingual berupa \nkanji dan kalimat dalam bahasa Jepang serta tanda nonlingual berupa warna-warna \ndominan dan karakter dalam cerita folklor yang diilustrasikan sebagai penumpang \nkereta api. Penggunaan bahasa Inggris di dalam poster digunakan agar penumpang \nasing dapat memahami konteks di dalam poster tersebut.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 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".