Ethical attitudes on human cloning among professionals in Taiwan and the policy implications for regulation.
Bibliographic record
Abstract
UNLABELLED: This research focused on understanding the attitudes toward human cloning in Taiwan among professionals in healthcare, law, and religion. DESIGN: The study was conducted utilizing a structured questionnaire. PARTICIPANTS: 220 healthcare professionals from two regional hospitals located in Taipei, 351 religious professionals in the northern Taiwan and 711 legal professionals were selected by to receive questionnaires. The valid response rate is 42.1% MAIN MEASUREMENTS: The questions were generated by an expert panel and represented major arguments in the human cloning debate. There were a total of six Likert scaled questions in the questionnaire. The responses were coded from 1 to 5 with 1 representing strong opposition to human cloning, 3 representing a neutral attitude; and 5 representing a strong favorable attitude toward human cloning. RESULTS: Healthcare professionals had the highest overall average score of 2.14 and the religious professionals had the lowest average at 1.58. All three categories of respondents' attitude toward cloning ranged from mild opposition to strong opposition to human cloning. The religious professionals were more strongly opposed to cloning. Age, education, and religion significantly influenced attitudes toward cloning. Professionals between fifty-one and sixty years old, those with less education, and Roman Catholic professionals were more strongly opposed to cloning. CONCLUSIONS: Religious professionals were more strongly opposed to human cloning than professionals in healthcare or law. Younger professionals as an age group demonstrated less opposition to human cloning. Regulation of human cloning will be influenced by professionals in healthcare, law, and religion, and the regulatory environment chosen now will play a pivotal role in influencing the acceptance of human cloning in the future.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".