Facing personal and environmental security challenges in post-conflict settings: The case-study of the Democratic Republic of the Congo
Bibliographic record
Abstract
Since , the Democratic Republic of the Congo (ex Zaire) has been caught into geostrategic battles, in particular in the Kivu region. The origin of those conflicts can be traced back to the insurrection of the Banyamulenge for the recognition of some of their denied civil and political rights that occurred in the aftermath of the Rwandan genocide. Some historians argue that the Banyamulenge are ethnic Tutsis, originally from Rwanda, who emigrated to the Kivu region prior to the colonial period. Others suggest that they arrived in Congo after the country had become a Belgian colony in , which would have disqualified them from the right to control communal land after the country’s independence, following the Congolese constitution. Despite the political deals recognizing their previously denied civil and political rights signed after the last two wars (– and –), reports by Freedom House, the United Kingdom Home Office and the Immigration and Refugee Board of Canada suggest that some Banyamulenge are perceived by other Congolese citizens as the main responsible of the Congo crisis. The purpose of the first part of this paper is twofold. Firstly, it seeks to understand the extent to which the post–colonial borders of the Democratic Republic of the Congo, in particular of its Kivu region, account for the recent armed conflicts between Congo and Rwanda, and secondly, it will examine the strengths and weaknesses of the measures adopted by the Congolese government for the recognition of the Banyamulenge’s political and civil rights from a conflict prevention perspective. These goals will be achieved through a review of available literature on the topic. The second part of the paper will cover aspects of emergencies linked to a Sustainable and Responsible Development in the Democratic Republic of Congo. In fact, Congo is one of the richest countries in the world in terms of gold and diamond mines, important minerals in the electronics industry such as Coltan, and in terms of biodiversity — flora and fauna. However, Congo is also one of the world’s most troubled and poorest countries. The major part of the rebels’ activities consists of abuses against civilians and illegal exploitation of natural resources, be they metals, ivory or wood. During the civil war in Rwanda, in the early nineties, over half a million of Rwandan refugees were forced by the violence of the fighting to take refuge in the Virunga National Park in the Democratic Republic of Congo: the forests were plundered of their timber and wildlife; even the mountain gorillas, endangered species, were killed and used as food. It should be noted that in the north of the Democratic Republic of Congo there is one of the world’s largest areas of the equatorial forest, hence, very important in the fight against climate change. One aspect that is often not fully taken into account in the study and analysis of conflicts is the fact that in addition to being a human and economic catastrophe, conflicts represent an ecological disaster.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".