Mapping research frontiers in gender and sustainability in agricultural development: a bibliometric review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Gender and sustainability are crucial in agriculture, which remains a significant source of global employment. However, urbanization, industrialization, and technological advancements have reshaped the sector, impacting labor dynamics and gender roles. Traditional agricultural labor faces challenges due to low wages, physically demanding tasks, and unfavorable working conditions. Addressing gender disparities and promoting inclusive work environments is essential for achieving sustainability. According to the ILO (International Labour Office) decent work encompasses productivity and equal employment opportunities for both genders. This study aims to review the literature on gender, sustainability and agricultural development using a bibliometric analysis of Scopus-indexed articles. The findings identify five main research domains: gender dynamics and roles, agriculture and climate change, sustainability and development, human and labor dynamics, and environmental and technological aspects. Additionally, four key scientific communities led the research: Gender studies, agricultural economics, environmental management, and rural sociology. Emerging research trends focus on gender roles in sustainable farming, environmental innovation, and labor governance in agriculture. Spain, the United Kingdom, United States, and Canada lead in knowledge production, contributing significantly to these research domains. This review highlights the importance of interdisciplinary approaches to address the complex issues of gender and sustainability in agriculture. It also specifies a target for expectations research, highlighting that the ILO’s definition of appropriate employment can guide efforts to improve gender equity and labor conditions, ultimately supporting sustainable development in the agricultural sector.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.023 | 0.088 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it