Bibliographic record
Abstract
For my project, I have constructed a faux field journal for my senior thesis. The journal depicts the journey of a fictional cultural anthropologist as he travels through East Asia; however, he encounters eight cryptids – mythological and unsubstantiated beings – that corrupt his sanity. The story begins in the Philippines and continues through Vietnam, Cambodia, Thailand, Laos, and China, ending in Mongolia. The purpose of the project was to promote my interest in originality by creating a horror story using cryptids. My research centered on cryptids that inhabited or were witnessed in Southeast and Northeast Asia. The cryptids were selected based on the environment in order to prevent a sub-theme of similarity in their habitats. For the building process, I constructed the book from a modern, leather-bound journal that I made to appear old and worn by bathing it in a large jar containing concentrated coffee and mud. I used an oven and lighter to apply burn marks and crisp edges to the journal’s covers and pages; blades to produce deep cuts and touch-ups; and standard loose ink and pencil for writing and illustrating. For the story, the deterioration of the character’s mentality was shown through word crowding, displacement, and the darkening of the illustrations as the story progressed.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.172 | 0.064 |
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".