Following money, mapping ‘development’: The opaque geographies of UK aid flows across the outsourcing assemblage
Bibliographic record
Abstract
This paper brings ‘follow the money’ approaches in economic geography into dialogue with perspectives from critical accounting to develop a methodology for opening the ‘black box’ of private sector development finance. Specifically, we engage with the infostructures that shape access to data around Official Development Assistance by the UK government. We focus on the impact of recent cuts to the aid budget, and large scale re-allocation of development spending through the Home Office towards ‘In-Donor Refugee Costs’, or asylum seeker and refugee support. Our methodology shows how development finance is channelled through the Home Office, becoming part of the reproduction of the UK’s outsourced hostile environment, at the same time that development contractors turn back to the UK to seek work in the context of a declining aid budget. As such, development capital becomes spatialized through the UK’s geographies of deprivation and asylum dispersal, while outsourcing giants are able to capture ‘excess profits’. Based on our methodological contribution, we also highlight how specialist development contractors are able to traverse the UK’s borders in the pursuit of aid-funded business, finding new domestic markets opening up in response to hardened borders and a declining overseas aid spend.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".