Bibliographic record
Abstract
Rezime : Nan peyi Ayiti, se pwofesyonèl sante modèn (sikyat, enfimyè, saj-fam, etc.) ak sikyat tradisyonèl (ongan, \nmanbo) ki bay moun ki gen pwoblèm sante mantal swen. Kategori aktè sa yo pa itilize menm teknik pou \nrive bay pwoblèm sa yon non epi pou rive fè tretman paske yo pa wè l menmjan. Malad yo ak paran yo gen \nplizyè chwa devan yo. Yo plis ale kay ongan ak manbo paske yo gen menm kwayans ak yo sou maladi ak sante. \nSe aprè echèk tretman konplèks yo konn fè, gen terapet tradisyonèl ki konn mande paran malad yo mennen yo \nkay doktè. Men, doktè ki ap travay nan sant sikyatrik yo prefere kenbe malad pa yo menm “pandan ventan” olye \nyo mande paran yo mennen yo wè yon ongan oubyen yon manbo. Entolerans ontolojik ak entolerans epistemik \nyo anpeche yo kolabore avèk moun sa yo. Nan atik sa a, nou pral gade ki modèl koz ak tretman moun ki ap bay \nswen yo itilize pou bay maladi foli sans, avan nou reflechi sou avantaj yon kolaborasyon epistemolojik ant yo ka \npote pou yo ak pou malad yo.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".