THE ETHICS OF INTERCOUNTRY ADOPTION: WHY IT MATTERS TO HEALTHCARE PROVIDERS AND BIOETHICISTS
Bibliographic record
Abstract
The goal of this paper is both modest and ambitious. The modest goal is to show that intercountry adoption should be considered by ethicists and healthcare providers. The more ambitious goal is to introduce the many ethical issues that intercountry adoption raises. Intercountry adoption is an alternative to medical, assisted reproduction option such as in vitro fertilization (IVF), intracytoplasmic sperm injection, third party egg and sperm donation and surrogacy. Health care providers working with assisted reproduction are in a unique position to introduce their clients to intercountry adoption; however, providers should only do so if intercountry adoption is ethically equal or superior to the alternatives. This paper first presents a brief history of intercountry adoption. The second section compares intercountry adoption with medical alternatives. The third section examines the unique ethical challenges that are not shared by other medical alternatives. The final section concludes that it is simplistic for a healthcare provider to promote intercountry adoption unconditionally; however, in situation where intercountry adoption is practiced conscientiously it poses no greater ethical concern than several medical alternatives. This conclusion is preliminary and is intended as a start for further discussion.
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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 | 0.058 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".