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Record W2595905976

Smart Peacekeeping: Toward Tech-Enabled UN Operations

2016· article· en· W2595905976 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsPeacekeepingCitizen journalismEmerging technologiesWork (physics)Field (mathematics)Information technologyBusinessEngineeringPolitical scienceComputer sciencePublic administrationLaw
DOInot available

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.268
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it