Bibliographic record
Abstract
Security sector reform is emerging as a central peacekeeping challenge. The creation of a professional security sector, properly mandated and resourced and subject to democratic control can contribute to the reconstruction societies affected by violent conflict. In contrast, peace is unlikely to be sustainable over the long-term in societies where these reforms are not undertaken. Recognizing the importance of security sector reform, the Pearson Peacekeeping Centre convened a round table of subject matter experts and practioners at our campus in Cornwallis, Nova Scotia between 29 November and 1 December 2000. Our objective was to enhance Canadian knowledge and expertise in this area. As well, the Centre hoped to build a constituency of institutions and organizations involved in international security sector reform programming from across the New Peacekeeping Partnership. The results of our deliberations are recorded in the following proceedings. Subject matter papers address key security sector reform issues, from defining the nature of our work to reviewing sectoral activities of Canadian government agencies and civil society organizations. The Concluding Summary provides an overview of our deliberations, including concrete steps to build on the knowledge and co-operative spirit developed at the event.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.428 | 0.204 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".