Evidence map›Paper›PMID 42038266›Full record

ArticleNetwork science (Cambridge University Press)2026

In search of common ground: Exploring value networks at the UNFCCC climate change talks.

Zack W Almquist, Benjamin Bagozzi, Daria Blinova, Zach Brown

Abstract read
In one paragraph

Article in Network science (Cambridge University Press), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zack W AlmquistDepartment of Sociology, University of Washington, USA.ORCID https://orcid.org/0000-0002-1967-123X
Benjamin BagozziDepartment of Political Science, University of Delaware, USA.
Daria BlinovaDepartment of Political Science, University of Delaware, USA.
Zach BrownDepartment of Sociology, University of Washington, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the values held by negotiating parties is central to the design and success of international climate change agreements. However, empirical understandings of these values - and the manners by which they structure negotiating countries' value networks and interactions over time - are severely limited. In addressing this shortcoming, this paper uses keyword-assisted topic models to extract value networks for the 13 most recent Conferences of the Parties (COPs) to the United Nations Framework Convention on Climate Change (UNFCCC). It then uses network analysis tools to unpack these networks in relation to influential values, countries, and time. In doing so, it demonstrates that countries' core climate change values (i) can be accurately recovered from COP High-level Segment (HLS) speeches and (ii) can, in turn, be used to understand the structure of negotiation networks at the UNFCCC. Analysis of the corresponding value networks for COPs 16-28 indicates that initially central values of "Fairness" and "Power" have increasingly given way to values associated with the "Environment" and "Achievement." Thus, countries at the UNFCCC have increasingly eschewed values associated with common but differentiated responsibilities in favor of a consensus over the urgency of collectively combating climate change. These and related insights illustrate our approach's potential for recovering and understanding value networks within climate change negotiations - a critical first step for any successful climate change agreement.

Indexed as

Climate changesocial networkstext-as-dataUNFCCCvalues

Identifiers

PMID42038266
PMCPMC13107239

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.