Prospective approach in determining potential economic sectors of East Timor as a new nation
Bibliographic record
Abstract
East Timor is one of the youngest nations in the world, having been established in 2002. The majority of East Timor's population works in the agricultural sector, yet the mining sector (oil and gas) plays a dominant role in contributing to the country's GDP. The management of oil revenue in East Timor is adapted from the model implemented in Norway, known as Norway Plus. Aware that oil and gas resources must be supported by other economic sectors in the long term, this study aims to analyze potential scenarios for developing East Timor's key economic sectors, as well as to propose potential policy pathways. The research approach uses prospective methods (SMIC-Prob analysis and MULTIPOL), which are suitable for policy formulation due to their future-oriented nature. Data collection was conducted through Focus Group Discussions (FGD) involving experts from both the government and academia. The results from the SMIC-Prob analysis show that the oil and gas sector indeed has the highest probability. However, in the long term, the agricultural sector emerges as a crucial alternative. Furthermore, the MULTIPOL analysis indicates that in scenarios focusing on the development of non-oil and gas sectors, policies should prioritize the development of local economic sectors. The government of East Timor is recommended to seriously develop non-oil and gas sectors by utilizing revenue from oil and gas extraction. Additionally, environmental governance is also a critical consideration to achieve sustainable, inclusive development.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".