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Record W2919003906 · doi:10.1017/s0022278x18000654

The humanitarian theatre: drought response during Ethiopia's low-intensity conflict of 2016

2019· article· en· W2919003906 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Modern African Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsConcordia University
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekErasmus Universiteit Rotterdam
KeywordsSomaliHumanitarian aidHumanitarian crisisState (computer science)Political scienceAuthoritarianismSovereigntyPoliticsPolitical economySociologyRefugeeDemocracyLaw

Abstract

fetched live from OpenAlex

Abstract This article aims to rekindle the debate on the politics of aid in the increasingly common – yet still under-studied – authoritarian and low-intensity conflict settings, detailing the case of Ethiopia in 2016, when a 50-year drought coincided with a wave of protests and a state of emergency. During four months of qualitative fieldwork in 2017, state, civil society, Ethiopian and international actors were approached – from humanitarian headquarters to communities in the Amhara, Oromiya and Somali regions. Research participants relayed stark discrepancies between the humanitarian theatre's ‘frontstage’, where disaster responders showcase an exemplary response, and its ‘backstage’, where they remove their frontstage masks and reflect on the information, the decision-making monopoly of the state and the intrusion of conflict dynamics into the humanitarian response. In humanitarian research and in policy, a collective conversation is necessary on where to draw the line between respect for governments’ sovereignty and the intrusion of humanitarian principles.

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.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.289
Teacher spread0.262 · 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