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Record W7124995360 · doi:10.1080/10361146.2026.2613447

A bibliometric analysis of the realities of Australian international development aid efforts in the Pacific region

2025· article· en· W7124995360 on OpenAlexaff
Mark Opoku Amankwa, Kwadwo Adusei‐Asante, Portia Nana Ama Brempong, Joseph Acheampong Ofori, Amidu Mutaru

Bibliographic record

VenueAustralian Journal of Political Science · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDevelopment aidInternational developmentInternational relationsDevelopment studiesAid effectiveness

Abstract

fetched live from OpenAlex

This study presents a bibliometric analysis of scholarly discourse on Australia’s international development aid efforts in the Pacific. Using HistCite and VOSviewer to examine 150 Web of Science articles, our study demonstrates a multifaceted, cross-disciplinary field spanning policy-oriented and health-focused journals as well as several aid-relevant broader development issues in the literature. Findings reveal the dominance of Australian universities in producing knowledge about Australia’s aid in the Pacific. We argue that knowledge production in the Pacific remains colonised, shaped by power dynamics that exist within the Pacific region. The findings suggest that Pacific-based academics should take the driver’s seat in aid research to promote more balanced and locally informed discussions on Australia's aid. This paper offers the most comprehensive bibliometric analysis to date of Pacific scholarship on Australian aid. It also exposes the systemic contradictions between Australia’s dominant aid discourse, localising research and gathering perspectives from Pacific scholars.

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)0.009
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.885
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.063
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.1150.191
Science and technology studies0.0020.002
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.356
Teacher spread0.308 · 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.

Study designNot applicable
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 routes1
Has abstractyes

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