Highlights in bioethics through 40 years: a quantitative analysis of top-cited journal articles
Bibliographic record
Abstract
Background The field of bioethics is constantly evolving. To investigate trends in the field of bioethics, we conducted a quantitative analysis of the top-cited articles in bioethical journals over the past 40 years. Methods Retrospective quantitative study of the 20 most cited bioethics articles published each year from 1975 to 2014 were conducted. Article samples were selected from a list of the most relevant 100 journals in the field of bioethics. Results In total, 800 top-cited articles between 1975 and 2014 in the domain of bioethics were retrieved and analysed. More than half of them were composed by single authors, but multiauthorship became more prevalent with time. The majority (84.5%) of these highly cited articles originated from the USA (65.3%), UK or Canada, though the proportion of other countries increased in recent years. Almost half (44.6%) of the highly cited articles belonged to the subfield of clinical ethics, but other subfields such as research ethics, public health ethics and neuroethics became more prominent. Overall, the distribution of Thesaurus keywords and subfields became more diverse over time, and the number of journals publishing top-cited articles doubled. Furthermore, the empirical ethics approach increased over time in our sample of top-cited articles. Conclusions In sum, the forefront of bioethics is getting more diversified, collaborative and international. The presumed ‘mainstream’ becomes less dominant over time, as more highly cited articles come from new subfields, discuss new topics, use more Bioethics Thesaurus keywords, more authors participate and more countries other than the USA contribute to bioethics journals.
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 | 0.023 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.054 | 0.054 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".