Holistic analysis of global feminism publications: A bibliometric evaluation of feminism literature between 1975 and 2017
Bibliographic record
Abstract
Bibliometrics is a relatively novel statistical branch investigating academic publications in a certain field. Although there has been an increasing popularity of bibliometric studies in recent years, scientific literature lacks a holistic analysis of feminism literature. To the best of our knowledge our study was the first bibliometric analysis of the publications in feminism literature. All data of this study was obtained from Web of Science databases. All documents produced in feminism literature between 1975 and 2017 were included. A total of 44,920 published articles were found. The peak year of feminism literature was 2017 with 3378 articles. English was the major language of the literature and it covered 83.91% of total documents. The United States of America (USA) dominated the area with 18,127 articles and covered 40.35% of all literature followed by the UK, Canada, Australia, Spain, Brazil and South Africa (n=5035, 3383, 2180, 777, 620 and 472 documents, respectively). Publications related to feminism were produced from almost all regions in the world except for some African and Asian countries. Canada was the most productive country with a score of 91.55 followed by the Australia, New Zealand and the UK (s=88, 81.11 and 75.63, respectively). Eun-Ok Im from the USA was the most prolific author with 40 articles in feminism field. The USA was the predominant country during all the period of 1975 to 2017. University of California System was the most contributor institution between 1975 and 2009 although University of London ranked the first after 2010. Developed countries dominated feminism literature and all 20 most contributor funding agencies were from developed countries. The researchers from developing and least-developed countries should be supported to produce new publications in the field of feminism. © 2021. All Rights Reserved.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.163 | 0.162 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".