Generative AI tools are reshaping academic writing. The central issue is not their use, but when AI-assisted text can still be recognised as legitimate scholarly work. This exploratory study examines how experienced academics evaluate legitimacy through three conditions: retention of human responsibility for core ideas, limitation of AI to supportive roles, and disclosure of its use. Data were collected from 25 participants through a questionnaire combining rating scales and open-ended responses. The findings show that legitimacy is conditional rather than binary. Participants accepted AI for drafting, rephrasing, and organising text, but expressed concern when it shaped arguments or interpretations. Across responses, three conditions consistently defined acceptable use: AI must support rather than replace intellectual work, authors must remain accountable for all claims, and AI involvement must be disclosed. Legitimacy, therefore, rests on ongoing professional judgment rather than fixed rules.

Education of women has come under focus as a means to transform gender and contribute to sustainable rural development in Pakistan. The aim of this ethnographic research is to explore the role of education in changing gender roles of Basti Rasoolpur, a rural community in south of Punjab. Research is conducted using qualitative approaches such as in-depth interview, participant observation, focus group discussion and informal interaction, examining women's involvement in household decision making, mobility, community involvement and economic life. Results show that education increases women's confidence, autonomy and ability to make a difference in their family's wellbeing, health, money matters and child education. The study reveals evolving attitudes, including greater acceptance of women's mobility and public participation, while maintaining social legitimacy and respectability. The findings show that women’s education strengthens social inclusion, economic resilience, and community development. These outcomes support sustainable rural development and future prosperity. 

Nurses play a key role in health emergency preparedness; however, there is insufficient literature related to nurse disaster readiness in resource-limited and high-risk environments. In GB, which is considered one of the most disaster-prone locations in South Asia, there is no empirically validated scale measuring DNCC. In this paper, a cross-case analysis has been conducted to determine if combinations of Education (EDU), Training (TRN), Organizational Support (ORG) and Structural Barriers (BAR) are necessary and sufficient to generate high or low levels of DNCC in nurses working at public hospitals. Fuzzy-set Qualitative Comparative Analysis (fsQCA) was used for data analysis using direct calibration based on percentile. It has been observed that none of the above-mentioned factors alone are sufficient to generate high DNCC. However, combination of different enabling factors can be responsible for high DNCC, while no organizational support has consistently shown up in configurations leading to low DNCC.