Bibliographic record
Abstract
The Darfur conflict is undoubtedly the most horrific contemporary humanitarian and security disaster. Unfortunately, the field of security studies has approached this conflict within policy-making, academic and military-circles as a gender-neutral social science. However, within this conflict women have played a larger role in defining how insecurity is understood, as security actors, as academics, as military personnel and as key members of international civil society. Therefore, this paper asks the central question – does the increased role of women affect how security is understood? Do women actors change how we deal with threats to security? Are there different threats that are relevant to women in security? This paper examines these questions by looking at ethnic conflict in Sudan through a gender-perspective. This research examines if a gendered approach to security studies changes how ethnic conflict is understood and addressed. Therefore, the paper makes several conclusions about the field of security studies. First, it argues that the field has been dominated by a top-down perspective, where structural level issues, such as geopolitical relationships, have dominated study of the Darfur conflict. Second, the paper recognizes that traditional definitions of power within the conflict have focused on material and military effect. Whereas, gendered forms of power, including sexual and structural violence, have played a large role in further subjecating the black population. Finally, it examines the role of women as security actors, in their policy, military and activist roles, showing how gendered solutions to security may be the most effective way of ending Darfur’s conflict.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".