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.
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 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 | 0.092 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.152 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".