Global governance innovation report 2023: redefining approaches to peace, security, and humanitarian action
Bibliographic record
Abstract
In introducing novel ideas for the September 2024 Summit of the Future and New Agenda for Peace, this report seeks to encourage more ambitious, forward looking thinking and deliberation on global governance renewal and innovation. The world needs better ways to manage its many, growing problems. Engaging new voices, instruments, networks, knowledge, and structures is the key to coping with today’s and future global challenges, which include, but are not limited to, renewed Great Power tensions, deepening Global North-South divides, virulent nationalism, runway climate change, and unconstrained artificial intelligence. Against this backdrop, the inaugural Global Governance Innovation Report (GGIR) aims to inform and advance debates on improving global governance, and to spur action to that end, drawing on insights from two new tools: a Global Governance Index and a Global Governance Survey. Encouraging greater ambition in preparations for the September 2024 Summit of the Future in New York and a New Agenda for Peace, the report offers proactive measures to better prevent, and failing that, limit the escalation of deadly conflict; reconsiders disarmament measures to boost conditions for conflict management and resolution; and proposes a next generation humanitarian action architecture to save more lives when conflict prevention and mitigation fail. Central to a strategy for change, GGIR’23 introduces five steps for mobilizing a broad-based, smart coalition of governments and civil society groups to maximize the generational opportunity afforded by next year’s Summit, to better ensure “the future we want and the United Nations we need” for present and future generations.
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 | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".