Local-global linkages in biodiversity governance: The regime complex of the convention on biological diversity agenda for nature pledges
Bibliographic record
Abstract
The trajectory of global biodiversity governance, culminating in the 2022 Kunming-Montreal Global Biodiversity Framework (KMGBF), reflects a pivot toward transformative change through a “whole-of-society” (WoS) approach. This approach integrates traditional multilateral negotiations with various self-organizing governance initiatives across the public, civil, and business spheres, forming additional layers within a biodiversity regime complex. While praised for its flexibility, horizontal linkages, and adaptability, an open question remains: Has this regime complex effectively delivered on its transformative promises? Using 718 biodiversity pledges submitted to the Action Agenda for Nature under the Convention on Biological Diversity—representing 1086 actors and 4109 connections—we applied social network and discourse analysis to map dynamic actor interactions and examine how they shape regime dynamics. Our findings reveal the emergence of a “middle-out” governance space, where non-state and sub-national actors act as intermediaries linking global commitments to local implementation. By visualizing the diffusion of participation, we identify potential leverage points where these actors can step forward as agents of change within the biodiversity regime. Yet despite these advances, network fragmentation persists—marked by duplication, misalignment, and weak cross-scale connectivity. Divergent discourses and weak ties between biodiversity status and actions further hinder systemic coherence. We argue that the regime complex must be reconceptualized beyond horizontal linkages to include vertical dimensions of governance. This study contributes to emerging network approaches in global biodiversity governance by identifying governance gaps and highlighting opportunities for systemic transformation. Strengthening alignment across levels and empowering middle-out actors are essential steps toward translating ambitious global biodiversity goals into effective, inclusive, and locally grounded actions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".