Timor-Leste, since its independence in 2002, has faced challenges in managing the growing number of civil servants, currently numbering over 13,000. Civil servants are classified under two regimes: General (GCR) and Special (SCR). This classification leads to disparities in salary, benefits, promotions, and performance evaluations, creating dissatisfaction among employees in both regimes. GCR employees enjoy regular salary increases and promotions based on performance, whereas SCR employees, despite earning higher salaries, do not receive the same opportunities for career advancement. This policy paper analyses these differences and recommends abolishing the dual regime structure. It proposes a unified system where salary progression, benefits, and promotions are based on education level, service length, and performance evaluations. Additional allowances should be provided to those working in rural areas to encourage retention and equitable service delivery. Regular external monitoring and audits are essential for effective performance management. Evidence from Indonesia supports these recommendations, showing that additional allowances and strong leadership improve employee performance and discipline.

Geochemical mapping of river sands in the Manufahi area of Timor-Leste revealed potential areas for future mineral exploration. River sand samples from the study area were collected and geochemically analyzed to identify anomalous concentration distributions of several valuable elements and locate potential target areas and geological formations that may host mineral deposits. The 26 major and minor elements were identified using wavelength-dispersive X-ray fluorescence. The river sands exhibited varying elemental concentrations, with Cr, Cu, Zn, and Ba showing deviations from the normal distribution patterns. Identification of geochemical anomalies is an important task in mineral exploration geochemistry. The mean + 2 standard deviations (mean+2STD), median + 2 median absolute deviations (median+2MAD), and Tukeyâs inner fence (TIF) methods were used to determine the geochemical thresholds. This study shows that TIF and principal component analysis (PCA) methods are highly effective in calculating appropriate threshold values and identifying relevant elemental associations. These approaches have proven useful for delineating target areas for mineral deposits, resulting in reliable outcomes. Four predicted target areas with high potential for deposits and mineralization anomalies of Cr, Cu, Ni, and Ba were delineated in the study area.

The quality assurance of malaria microscopy unit was established by National Malaria Control Program, Ministry of Health (MoH) in 2009 to respond to the internal malaria microscopy validation process, in microscopy training for malaria diagnosis in the country, monitoring and supervision, and other relevant activities regarding laboratory diagnosis of malaria in Timor-Leste is described. Trained laboratory technicians who are working in the relevant unit. There are two ways of malaria microscopy validation process such as onsite validation and monthly validation after received malaria blood smear from senior analyst at the municipality level. The aim of this work is to describe the quality assurance of malaria microscopy system in Timor-Leste. The management of quality assurance service support, the importance of quality assurance of malaria microscopy, the role of quality assurance of malaria microscopy, challenge and opportunities encountered by quality assurance microscopy unit, National Health Laboratory are highlighted. Key words: Quality assurance of malaria microscopy, system, Timor-Leste.

This paper systematically examines Chinese academic discourse that concurrently addresses Indonesia and Timor-Leste, analysing 137 articles from the China National Knowledge Infrastructure (CNKI) and indexed by the Chinese Social Sciences Citation Index (CSSCI). The analysis reveals significant publication trends, particularly during periods of heightened geopolitical and economic engagement, such as the global expansion of the Belt and Road Initiative (BRI). It identifies predominant themes such as economic cooperation, security concerns, and historical-cultural dynamics. The findings highlight China’s strategic interests and leadership aspirations in Southeast Asia， providing insights into how Chinese scholars perceive and articulate China’s strategic interests and diplomatic efforts in the region involving Indonesia and Timor-Leste.

Violence against women or gender-based violence (GBV) is a major public health issue facing women and girls in different settings and is reported to have worsened globally during the COVID-19 pandemic. Despite the impact of the COVID-19 pandemic on increased violence against women in general, which has been reported in many settings globally, there is a paucity of evidence of its impact on violence against highly vulnerable women living with HIV or tuberculosis (TB). Using a qualitative design, this study aimed to explore the views and experiences of women living with HIV (n=19) or TB (n=23) in Timor Leste regarding the GBV they faced during the COVID-19 pandemic. They were recruited using the snowballing sampling technique. Data were collected using one-on-one in-depth interviews and focus group discussions. The five steps of qualitative data analysis suggested in Ritchie and Spencer’s analysis framework were employed to guide the analysis of the findings. Findings indicated that women in this study experienced intensified physical, verbal, sexual and psychological violence by their partners, spouses, in-laws, and parents or other family members during the COVID-19 pandemic. Several prominent risk factors that worsened violence against women during the pandemic were (i) HIV or TB-positive status, (ii) traditional gender roles or responsibilities and expectations, (iii) economic and financial difficulties reflected in the loss of jobs and incomes due to the pandemic, and (iv) individual factors such as jealousy and increased alcohol drinking developed during the lockdowns. The findings underscore the urgent need for multifaceted interventions to address GBV, which should encompass challenging traditional gender norms, addressing economic inequalities, and targeting individual-level risk factors. The findings also indicate the need for the development of robust monitoring and evaluation systems to assess the effectiveness of policies and interventions addressing GBV where the results can inform future improvement. The findings also indicate the need to include GBV in the protocol or guidelines for HIV and TB management. Future large-scale quantitative studies to capture the magnitude and specific drivers of GBV against women living with HIV and TB during the pandemic are recommended.

This study utilizes a combination of optical PlanetScope imagery and Synthetic Aperture Radar (SAR) data from Sentinel-1 to investigate land cover changes in Dili, Timor-Leste, following Tropical Cyclone Seroja. The primary objective is to evaluate how integrating these datasets enhances disaster monitoring and response in flood-affected areas. The Random Forest classifier, known for its robustness in handling high-dimensional and noisy data, was applied to classify land cover into six distinct classes: vegetation, water, built-up areas, bare soil, clouds, and shadows. The classification was performed across three phases—pre-disaster, post-disaster, and recovery—using Google Earth Engine (GEE). Binary segmentation using Otsu thresholding was applied to the SAR images to refine the classification by delineating water bodies from non-water areas. The study achieved high classification accuracy, with overall accuracy ranging from 97.2% to 98.7%, and the Kappa index, which measures agreement between classified and reference data, remained consistently strong, indicating reliable model performance. Key results include a significant increase in water bodies and extensive damage to vegetation and built-up areas during the post-disaster phase, followed by partial recovery in the subsequent period. Despite the high accuracy, urban areas posed classification challenges due to misclassification between built-up and bare soil categories. This research offers a methodological framework for integrating optical and SAR data with machine learning for land cover change detection in post-disaster scenarios. The societal benefits of this study include improved disaster preparedness, enhanced recovery planning, and valuable insights into flood impact mitigation in regions like Timor-Leste, where technical and data limitations persist.