Bibliographic record
Abstract
Even in the current context of financial constraints and challenging Member State dynamics at the UN, the next 12 months should be be seized as an important time for realizing pragmatic improvement in how the international community assists countries emerging from conflict.The Civilian Capacity (CIVCAP) initiative represents a real opportunity to drive concrete change on issues long recognized as deficient.CIVCAP is an important chance to depart from tired and often ineffective approaches to providing technical support in fragile settings.There are practical steps policy-makers can take to support a strategic shift in how peacebuilding and post-conflict assistance is provided.Since March 2011, CIVCAP has remained a prominent agenda item at the United Nations.The key findings and main recommendations of the CIVCAP report were strongly supported by the UN Secretary-General and in May 2012 the CIVCAP process was officially recognized by the 193 Member States of the General Assembly.Since that time, the UN and partners have engaged in intensive policy consultations and have sought to identify solutions both in the field and for systemic challenges.This policy brief presents developments in 2012 and it spotlights the CAPMATCH consultation with the Training and Rostering Community held in June 2012, which was supported by NUPI and co-hosted by the Permanent Missions of Indonesia and Canada to the United Nations.The coming General Assembly session will be important for maintaining momentum for the CIVCAP agenda.This policy brief identifies three broad opportunities for policy makers to help deliver short-term results for CIV-CAP and to set the stage for further reform:1.At the upcoming 67thGeneral Assembly session; 2. In support of select field programmes; and 3.In support of the CAPMATCH launch in mid-September 2012
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 | 0.044 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.032 | 0.023 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.035 | 0.033 |
| Insufficient payload (model declined to judge) | 0.095 | 0.043 |
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".