Bibliographic record
Abstract
It was a warm September's evening, just before dusk, and my fami ly and I were visi t ing friends in Fulton. The topic of fo lk art entered our conversa-t ion because I was interested in studying a local t radi t ional ar t is t for a f ie ld reserach project in my Mater ial Folk Culture seminar taught at UMC by Howard Marshall. My f r iend got up f rom the table and asked me to come wi th her. We hurried to the car, and she drove me to the "Old Jef f C i ty Road. " About a quarter of a mile down the road, she slowed her car and pointed to some old boards on a fence by the side of the road. "This is Jesse Howard's place. The fence used to be covered w i th signs. " She then drove up the road about a block and crept to a stop. "You see that l i t t l e house back there? When I was in high school, I heard that Jesse's son had died in the war, and he [Jesse] had his body in there. He guarded i t w i th a shot gun at night. " I knew the story was probably false. Darkness had sett led, making i t d i f f i cu l t for me to see, but her story sparked my imagina-t ion. A f te r making a U-turn on the narrow road, she pointed to signs that
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.173 | 0.055 |
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".