Bibliographic record
Abstract
This paper is a companion to my thesis film, CoCo Nut. Thesis film is the third animation I made at RIT. From Position (one quarter film) to Noble (two quarter film), the core focus of my story moved from individuality to family; so for my third and last film at RIT, I wanted to make my country the core essence of my story. The idea of my thesis film is about Taiwan, where I was born and raised. It is a story on the importance of cooperation. Due to our unique historical and political background, the two ruling parties of Taiwan, Kuomintang (the National Party) and Minjintang (the Democratic Progress Party) are constantly engaging in political struggles. Mutual checks and balances are common factors in a democratic society, however if the ideological disputes are inappropriately exploited, the opposing views are taken to the extreme and people’s livelihood can only be negatively impacted as a result. This is what is happening in Taiwan right now. People of the same race and culture are split into opposing groups by ideological propagandas inspired by two parties. Disunity only results in degraded quality of life. The two characters of my story, Kuo (blue) and Min (green), are shown chased by a sea monster to an island. To quench their thirst, they cooperate to fetch the last few coconuts left on the island. When both of them wanted to possess the biggest coconut, they begin a fight that almost results in their death—completely unaware that the sea monster is still lurking in the water, waiting to swallow them. At last, they realize that the only chance of survival is through collaboration. They relinquish their dispute, team up together, defeat the sea monster, break the coconut with the monster’s teeth, and share the sweet coconut juice. Through my animation, I wish to convey to my audience the simple idea that mutual tolerance and cooperation are vital for all of us sharing this tiny planet, particularly for those of us from Taiwan. It is only through mutual tolerance and co-effort can we make our tiny home a better place to live.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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 teacher head, 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".