Bibliographic record
Abstract
Rezime : Fanm lavil ki ale akouche andeyò pou zafè pa bon ak fanm andeyò ki akouche lakay yo jere doulè \nakouchman san yo pa pran medikaman. Anpil nan fanm sa yo se potestan yo ye. Gen ladan yo tou ki se vodouyizan. \nMenmsi yo pa vodouyizan, gen nan yo ki konn fè rele ongan oubyen manbo lè yo santi doulè akouchman \nan pa yon bagay ki nòmal. Nosyon akouchman san doulè pa egziste pou anpil fanm andeyò, sitou pou sa ki pa \ngen mwayen yo. Anpil ladan yo fè nenpòt dis timoun lakay yo. Matwòn yo ede fanm sa yo jere doulè yo san yo \npa itilize medikaman. Gen anpil nan fanm sa yo ki te akouche lakay yo, paske al akouche lopital potko alamòd. \nGenyen ki prefere akouche lakay yo malgre gon lopital ki rele Mache Kabrit ki pa twò lwen yo, yon lopital ki bay \nfanm ki ap akouche peridiral pou redui doulè yo, daprè sa yon matwòn Benè te di nou. Nan atik sa a, nou dekri \nkijan matwòn ak fanm yo jere doulè akouchman lakay ; nou bay rezon ki koz anpil fanm rete akouche lakay yo \nandeyò epi nou eseye konprann ki sans yo bay doulè yo ak benefis siko-espirityèl yo jwenn ladan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
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; both teacher heads agree on what is shown here.
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".