Reimagining Humanitarianism and Refugee Research: Decolonisation or Symbolic Gesture?
Bibliographic record
Abstract
We come to the question of the extent to which the decolonisation of humanitarianism and refugee-related research is meaningful or tokenistic, and to what effect, as two co-authors from different backgrounds, contexts and upbringings. In this article, we present three points for consideration for practitioners and scholars engaging with critical humanitarian and refugee-related research based on our experiences. First, we propose that decolonial discourse has become co-opted but remains a catalyst for more significant conversations. Second, we argue that most scholarship that seeks to decolonise aid is not being generated by people who are at the margins, despite these people having lived experience of forced displacement. Third, we call on practitioners and researchers working in the field of critical humanitarian studies, and wider refugee research more broadly, to move towards what can be conceptualised as ‘constructive complicity’. This means ceding and redistributing power. As academics, aid practitioners and teachers, we wear multiple hats and put forward ideas to address issues we see in refugee-related research. However, we recognise that there is a need for a plurality of voices, contestations and critiques; our ideas are ever-changing, and we write this in the spirit of welcoming further conversation.
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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 | 0.033 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.183 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".