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Record W4414183552 · doi:10.3390/cli13090187

Establishment of Transboundary Partnerships in an International Climate Adaptation Project

2025· article· en· W4414183552 on OpenAlexafffund
Fowzia Gulshana Rashid Lopa, Dan L. Johnson

Bibliographic record

VenueClimate · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of Lethbridge
FundersUniversity of Lethbridge
KeywordsGeneral partnershipAdaptation (eye)Scope (computer science)Transparency (behavior)StakeholderStakeholder engagementClimate change adaptationFocus group

Abstract

fetched live from OpenAlex

The transboundary partnership encourages multi-stakeholder efforts to mobilize adaptation funding and services for adaptation governance. However, challenges exist in scope, equality, and transparency when engaging these stakeholders. Few studies have examined the detailed coordination among multi-stakeholders and the performance of their partnerships, creating an opportunity to understand how multi-stakeholders contribute to and manage efforts for future adaptation projects. This study focused on a transboundary action research project on climate adaptation, analyzing the partnership structure, stakeholder arrangements, coordination scenarios, and the role of the partnership within it. It included interviews with project personnel and focus group discussions with community members at the project sites. The results showed that the project emphasized collaboration while maintaining both vertical and horizontal coordination. The donor maintained vertical coordination for monitoring but collaborated on budget adjustments, funding, identifying challenges, and developing strategies to improve the partnership. Partners continued horizontal coordination by sharing responsibilities and taking leadership roles in synthesizing research reports. Additionally, this project facilitated community participation in piloting the adaptation intervention. Although this partnership aimed to establish collaboration, it remains far from effectively liaising with national-level governments to maximize the benefits of adaptation technology. Future research should expand this scope to promote sustainable development.

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 imitation

Not 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.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.004
Scholarly communication0.0040.004
Open science0.0010.013
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.080
GPT teacher head0.381
Teacher spread0.301 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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