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Record W4413451250 · doi:10.1080/01436597.2025.2540523

Against the charge of charity: refugee-led organisations, localisation and decolonising humanitarianism

2025· article· en· W4413451250 on OpenAlexafffund
Merve Erdilmen

Bibliographic record

VenueThird World Quarterly · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsMcGill University
FundersFonds de recherche du Québec
KeywordsRefugeePolitical scienceCharge (physics)LawPhysics

Abstract

fetched live from OpenAlex

Two of the main recent trends in humanitarianism have been the increasing focus on decolonialisation and localisation of humanitarian assistance. Donors have committed to raising funding for local actors, especially to refugee-led organisations, with the hopes of tackling colonial power inequalities in humanitarianism and empowering local actors. Yet, the narratives used to maintain the hegemonic understanding of humanitarianism in localisation efforts and dismiss refugee-led organisations have not been comprehensively studied. Drawing on 130 interviews with refugee-led organisations, non-governmental organisations, international organisations and state officials in Turkey, this article shows that by characterising refugee-led organisations as charities with an assumed religious agenda, instead of humanitarian actors, national non-governmental organisations disparage these actors. I argue that the preconceived idea that refugee-led organisations do not adhere to traditional humanitarian principles fuels other non-­governmental organisations’ labelling of refugee-led organisations as charities, but this dismissal is also driven by worries about competition in the humanitarian sector. Adopting a decolonial framework, I assert that the reluctance to shift more power and resources to refugee communities is not only about ideology but also about political economy, an under-examined factor in the literature to date.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.288
Teacher spread0.276 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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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