Ba ne’bé: where are you going? the changing nature of United Nations peacekeeping in Timor Leste
Bibliographic record
Abstract
The United Nations peacekeeping intervention into Timor Leste following September 1999 signalled a victory for the 24-year Timorese struggle for independence. To date most evaluations of this intervention have taken “problem-solving” approaches, which have primarily considered how to improve the effectiveness of UN peacekeeping operations. This has left a gap about the impact of the UN’s overall strategic approach to political reconstruction in transitional societies recovering from chaos. In order to fill this gap this research draws upon the lessons of intrastate conflict and state-failure in post-colonial states during the 1990s and uses an institutional peace-building framework to evaluate the impact of the UN’s political reconstruction efforts in Timor Leste. This study traces Timor Leste’s post-conflict state-building through different stages of post-conflict state-building between 1999 and 2005 and examines how new patterns of political conflict have changed. One of the principal areas of consideration is the role of democratisation as a method of transferring potentially violent factional conflict into a peaceful rule-governed institutional setting. Also considered is the role of administrative and political decentralisation as a method of consolidating post-conflict peace by strengthening the legitimacy of a new state from the “bottom upwards”. By doing so, this study contributes to the growing interest among academics and peacekeeping practitioners about the role of participatory peacekeeping interventions into post-conflict societies. This work also assesses how and the extent to which UN and international aid agencies contributed to achieving sustainable peace and development in Timor Leste through institutional peace-building. Timor Leste continues to face reconstruction challenges peculiar to its history of occupation and resistance, which threaten to undermine the successes of state-building. Nevertheless, this thesis argues that state-building under the tutelage of the UN was a tremendous success. The findings drawn offer valuable political reconstruction lessons for Timor Leste, as well as other post-conflict societies, that will help to consolidate transitions from conflict to peace.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".