Bibliographic record
Abstract
This paper considers how an intervening security force can implement the relevant components of the suite of United Nations Security Council resolutions on Women, Peace and Security (WPS). The analytical framework of the paper is a generic operational cycle comprised of preplanning, planning, conduct, and transition. Specific tasks identified in the resolutions are organised in this generic operational cycle. The tasks are those commonly led by security forces, or directed by government, and include: conflict analysis or intelligence; deliberate planning; force structure; population protection; female engagement; support to the rule of law; security sector reform; and disarmament, demobilisation and reintegration. This paper focuses on the experiences of the Australian Defence Force, with additional examples from militaries of Canada, Ireland, Sweden and the United States as well as organisational experiences from NATO and the United Nations. The paper draws on operations including, but not limited to, in Afghanistan, Rwanda, Yugoslavia and East Timor. Overall, the paper makes a unique contribution to the military operationalisation of the WPS agenda.
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 | 0.028 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".