Twenty years of the Inter-agency Network for Education in Emergencies: towards a new ontology and epistemology
Bibliographic record
Abstract
This paper reflects on the role of the Inter-agency Network for Education in Emergencies (INEE) by analysing and presenting a critique of its report ‘20 Years of INEE: Achievements and Challenges in Education in Emergencies’. Despite the strides achieved in highlighting the importance of education in humanitarian crises, we identify four critical points related to the ontology and epistemology of Education in Emergencies with a specific focus on refugee education: First, the oxymoron between short-term humanitarianism and future-oriented education, second, the purpose of education; third, the role of knowledge production within INEE as a primary agenda-setter; and fourth, how the INEE as firmly embedded in the humanitarian system reproduces unequal power dynamics. In conclusion and by using a decoloniality continuum (Abdelnour and Abu Moghli [2021]. “Researching Violent Contexts: A Call for Political Reflexivity.” Organization. doi:10.1177/13505084211030646) ranging from complicity to liberation, we offer different possibilities for INEE to address the four critical points and the potential for decolonising the field.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.102 |
| Scholarly communication | 0.018 | 0.030 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".