Bibliographic record
Abstract
Genre: Medicine/huru. I and Ebbe (Pidhu) had asked Longge to tell about her 'huru' a few days earlier. We arrived in the morning 15 Sept -15 by her house in the seaside kampong Dosalanga, and had a chat and coffe as is the custom. Then we recorded Ngole, sitting on a large bamboo plattform, partly shielded from the sea breeze. Ngole told nine 'huru', which is quite many. All is in one file and her narration is interrupted by the recording personnel (SD), to extract more information, and to ask if she hasn't forgotten any other 'huru'. The way that she performs the nine huru are all similar to each other: all are hung as curses in theplantation, and all use similar bhulu wao adat prayers. I have to ask about common things about 'huru' when the interviewed does not tell herself, or forgets. Recorded with the H4N Zoom because we were sitting outside. Present: Teodorus Ngajo (her son or neighbour), and two kids, Ike and Rike, listening in. Ngajo is also heard in the recording. Pidhu had left.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".