Context/Introduction

Indigenous iTaukei women in Fiji play a crucial role in small-scale fisheries, significantly contributing to household food security and livelihoods. Despite their essential contributions, their work often goes unrecognized. This case study highlights their critical role and the challenges they face in the fisheries sector (Thomas et al., 2021).

Approaches and Strategies

The study, conducted between 2017 and 2018, involved over 1,200 iTaukei women fishers from 113 villages across 11 provinces. Data was collected through structured questionnaires and focus group discussions, targeting multiple habitats including mangroves, mud flats, coral reefs, and the open ocean (Thomas et al., 2021).

Key Strategies:

• Diverse Data Collection: Targeting multiple habitats to gather comprehensive data.

• Gender-Disaggregated Data: Collecting data specifically on women's fishing activities to highlight their contributions.

• Inclusive Methodology: Engaging women in the data collection process to ensure accurate and comprehensive information.

Practical Tips

• Disaggregate Data by Gender: Ensure data collection methods include gender-disaggregated indicators such as access to fishing equipment, time spent in household work, and income generated from fishing activities.

• Engage Women Directly: Involve women in the data collection process to gather accurate information and empower them through active participation.

• Highlight Economic Contributions: Document and emphasize the economic contributions of women fishers to household and community well-being.

• Address Barriers: Identify and address barriers faced by women fishers, such as limited access to fishing gear, safety concerns, and exclusion from decision-making processes.

Limitations

• Geographical Spread: Covering a wide geographical area posed logistical challenges, making it difficult to reach all targeted villages.

• Cultural Barriers: Cultural norms and gender roles sometimes limited women's willingness to participate openly in the research.

• Resource Constraints: Limited funding and resources constrained the scale and depth of data collection and analysis.