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Record W4361208928 · doi:10.1080/14767724.2023.2191936

Twenty years of the Inter-agency Network for Education in Emergencies: towards a new ontology and epistemology

2023· article· en· W4361208928 on OpenAlexfundno aff
Cathrine Brun, Maha Shuayb

Bibliographic record

VenueGlobalisation Societies and Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsnot available
FundersEconomic and Social Research CouncilBritish AcademyInternational Development Research Centre
KeywordsAgency (philosophy)OntologyComplicityReflexivitySociologyPoliticsPerspectivismHumanitarian aidContradictionKnowledge productionPolitical scienceEpistemologySocial scienceLawKnowledge management

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.355
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2023
Admission routes1
Has abstractyes

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