Bibliographic record
Abstract
Cette courte histoire souleve des questions ethiques quant a la demande d'une femme pour de l'assistance medicale a tomber enceinte. Dans ce recit fictif, une femme de 34 ans a essaye de tomber enceinte pendant la derniere annee. Son mari aimerait continuer a essayer pour une annee de plus, mais la femme perd patience. Elle rend visite a un obstetricien gynecologue et demande l'insemination artificielle. Elle n'a pas l'intention de parler a son mari de cette assistance medicale. Ce medecin a aide des femmes celibataires, des couples lesbiens et des couples maries durant leur grossesse, mais il se sent en conflit par rapport a cette demande. Le medecin et la femme discutent de leurs preoccupations et des plans possibles. Puis, ils decident d'un plan d'action. D'une maniere creative, cette histoire a pour but de donner vie a des questions ethiques sur la procreation assistee, les relations complexes, les choix individuels, les attitudes sans jugement, la tromperie, la confidentialite, les connexions genetiques et les parents sociaux. Cependant, cette histoire n'est pas un cas classique qui illustre un probleme d'ethique clairement defini. Au contraire, l'histoire montre que certaines idees ethiques communes ne correspondent pas tout a fait a l'experience des personnages et aux reactions des lecteurs.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.027 | 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; 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".