Global Research on Smoking and Pregnancy—A Scientometric and Gender Analysis
Bibliographic record
Abstract
The exposure to tobacco smoke during pregnancy is considered to be amongst the most harmful avoidable risk factors. In this scientometric and gender study scientific data on smoking and pregnancy was analyzed using a variety of objective scientometric methods like the number of scientific contributions, the number of citations and the modified h-index in combination with gender-specific investigations. Covering a time period from 1900 to 2012, publishing activities of 27,955 authors, institutions and countries, reception within the international scientific community and its reactions were analyzed and interpreted. Out of 10,043 publications the highest number of scientific works were published in the USA (35.5%), followed by the UK (9.9%) and Canada (5.3%). These nations also achieve the highest modified h-indices of 128, 79 and 62 and the highest citation rates of 41.4%, 8.6% and 5.3%, respectively. Out of 12,596 scientists 6,935 are female (55.1%), however they account for no more than 49.7% of publications (12,470) and 42.8% of citations (172,733). The highest percentage of female experts about smoking and pregnancy is found in Australasia (60.7%), while the lowest is found in Asia (41.9%). The findings of the study indicate an increase in gender equality as well as in quantity and quality of international scientific research about smoking and pregnancy in the future.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.056 | 0.083 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".