Bibliographic record
Abstract
An Audio-Visual Prophecy: Arcane - The Prestigious Concert Scene The topic of my video essay is to take a close examination on the prestigious concert scene on Arcane: League of Legends. To see how the use of the mise-en-scene and audio-visual language helps to make foreshadowing on characters’ behaviors. The video essay illustrates the changing psychological states of several main characters (Jess, Mel, Hemerdinger and several councilors) based on their lines and action details. Through the analysis of cinematic elements like mise-en-scene, background music, lighting and staging provide in-depth observations and analysis of how the film uses superb audio-visual language to foreshadow the actions and situations of different characters later in the story. I also added tons of arrows and circles to highlight the significant elements in each frame to cooperate with my voiceover and helps focusing audiences’ attentions as well. Prof. Daniel in FILM 456 The Video Essay provided me a lot of advice, especially about the copyright issue about the editing of background music after the video was uploaded to the public platform. Some of the points Prof. Daniel mentioned in the lecture also helped me to better plan the structure and framework of the whole video.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".