Mapping the literature on women’s unpaid work : A bibliometric analysis using VOSviewer
Bibliographic record
Abstract
The role of women has transformed from primary caregivers to primary earners in the society. The growth of studies on women’s issues has been continuous research since decades. The research interest in unpaid work has seen increased collaboration, profound familiarity with influential authors, and access to relevant literature, especially for new researchers focusing on this issue. The present paper has performed a bibliometric analysis and maps the research landscape based on the published literature on women’s unpaid work, using R-biblioshiny program and VOS viewer software. It analyses, citation patterns authorship and affiliation and other parameters. The study is based on 1281 articles on women’s unpaid work drawn from the Scopus database, published between 2001and 2024.The study on women’s unpaid labour shows a rising trajectory with an increased volume of productive growth. More citations are from “Feminist Economics” journal, and identified 10 authors, the most co-cited cluster of researchers in the said field. The authors’ affiliations include prestigious institutions such as the University of Oxford, Flinders University, Stockholm University, and Australian National University. The study finds international collaboration more among authors from the USA, India, Australia, the U.K., Spain, Germany and Canada. The study has identified important keywords in the area, that includes “time poverty,” “parenthood,” “informal care,” “time allocation,” and “work-life balance.”
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.237 | 0.218 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".