Decolonizing Data Relations: On the Moral Economy of Data Sharing in Palestinian Refugee Camps
Bibliographic record
Abstract
Background This article interrogates the critical intersection of measurement, datafication, and value extraction in humanitarian settings, drawing on empirical examples of data sharing in Palestinian camps in Lebanon. Analysis Building on decolonial theory and post-humanist perspectives, the article offers a critical rereading of the moral economy as historically situated transversal practice and explores how the nonlinear transition of lived and embodied knowledge into and out of data (re)configures the calculus of reciprocity, justice, and fairness in the anticolonial struggle of Palestinians. Conclusion and implications The article introduces the concept of “ethico-political substance” to problematize the historical entanglement of social ontologies, coloniality, and power-knowledge and to show a constitutive split between data and its subjects, which continues to undermine the political possibilities of datafication to this day.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".