Explorando los estudios de la revista Gender Work and Organization: una revisión bibliométrica
Bibliographic record
Abstract
This article focused on analysing the publications of Gender, Work and Organization Journal, which has become a space for socialising research on gender relations in organisations since 1994, considering emerging issues in various countries and labour markets in order to contribute to discussions on this subject. This quantitative research used a bibliometric development in which 318 articles corresponding to 18 issues of the journal between 2020 and 2022 were systematised, with the aim of identifying the most outstanding trends and themes in the journal in order to present a context for the academic community interested in publishing in this area of research. From the analysis of the sample, 12 themes were established that contain 53 sub-themes that interrelate problem axes where qualitative research bets related to: diverse gender issues (34%), feminism (12%), intersectionality (10%) and diversity (10%), the categories that complete the remaining 34% had a participation in the total of works between 1% and 6%, highlighting the growing interest in developing analyzes based on intersectionality and the conditions experienced during the Covid-19 pandemic. Also, it was evidenced that of 712 authors, 427 were women and in terms of the institutional affiliation of the authors they continue to be from universities located in Anglo-Saxon contexts such as the United Kingdom, the United States, Canada and Australia, considering that the GWO journal publishes works written in English.
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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 | 0.046 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.120 | 0.165 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.006 |
| 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".