Homesickness: From Truth to Aesthetics
Bibliographic record
Abstract
This thesis delves into the exploration of transcendental homesickness, the emotional distress experienced when separated from home, extends beyond mere physical absence; it encompasses the profound loneliness that compels us to seek meaning in our existence, understand the origins of everything, and contemplate our destination. Through tea - the cultural lifeblood coursing through the veins of Chinese, this exploration examines how we can elevate our meaning of existence to an aesthetic experience that fosters connections with the external world, the cosmos, and brings a sense of home. \nEmploying a comparative methodology, the study brings together ancient Chinese thought, Daoism, with early Western thought to modern cosmology and ontology, examining their similarities and differences in addressing the relationship between humans and the external world. As the outcome of the MFA research-creation, the exploration takes a tangible form through the creation of hundreds of drinking vessels and the arrangement of red tea generously contributed by our community with blessings and love, translating our profound connection with the objects surrounding us into a visual language, resonating with our transcendental loneliness as individuals, communities, and human beings.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".