Bibliographic record
Abstract
As the world’s technological revolution proceeds, the United Nations can benefit immensely from a plethora of technologies to assist its peace operations. Fortunately, significant progress is being made. The UN has adopted a strategy for technology and peacekeeping and is showing the will and the means to implement it. New concepts, such as “technology-contributing countries” and “participatory peacekeeping” through new information technology, can improve peace operations. New technologies can also help UN field workers “live, move, and work” more effectively and safely, creating the possibility of the “digital peacekeeper.” This report provides an overview of technological capabilities and how they are being used, explores progress to date and key challenges, and offers a set of practical recommendations. These recommendations include several general principles, such as to: - Seek the buy-in of host countries and local populations so locals support the technologies; - Use greater feedback and reach-back to UN headquarters and other international supporters, made easier as technology allows more information processing and support from farther away; - Develop life-cycle equipment management, encouraging a systematic approach that maximizes technological potential; and - Manage expectations so that some failures can be tolerated along the road to success and so innovation can flourish without unreasonable fears. Beyond these general principles, it proposes ideas for new activities and processes: - At UN headquarters, develop a “solutions farm” and a “tech watch” with “tech scouts,” annual reviews of UN technology and innovation, technology selection criteria, cooperation with research and development institutes, and national testing and evaluation centers. - In the field, institute testing of new equipment, “proofs of concept” and pilot projects, demonstration kits, technology lessons-learned reporting, and special technological missions. - Engage troop- and police-contributing countries by incentivizing them to bring in effective modern equipment, providing them training to foster technological expertise, and encouraging technology-contributing countries to assist them. - Engage external actors and vendors by hosting a technology fair or “rodeo” and supporting a “hackathon” for smartphone and tablet app-developers on useful applications for peacekeeping.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".