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Record W2982105615 · doi:10.4018/ijdrem.2019070102

Managing Humanitarian Aid

2019· article· en· W2982105615 on OpenAlexaffabout
Ame Khin May-Kyawt

Bibliographic record

VenueInternational Journal of Disaster Response and Emergency Management · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsYork University
Fundersnot available
KeywordsHumanitarian aidContext (archaeology)DiasporaPolitical scienceGovernment (linguistics)Disaster responsePublic relationsEmergency managementHumanitarian crisisPublic administrationRefugeeGeographyLaw

Abstract

fetched live from OpenAlex

This article contributes to an overall understanding of the challenges faced by humanitarian aid international non-government organizations (INGOs) in specific culturally context-sensitive regions of Myanmar. This research is based on a review of literature, relevant case study analysis, and on ten semi-structured interviews with the humanitarian activists of the Myanmar Diaspora in Canada. The author investigates the following research question: To what extent does “cultural context” play a crucial role in managing humanitarian aid during disaster response operations in a given affected area, and how does it consequently link to the challenges of humanitarian aid INGOs in Myanmar? Based on the findings, a culturally appropriate framework will be introduced for the efficacy of humanitarian aid INGOs when implementing disaster response operations in Myanmar.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0060.003
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.014
GPT teacher head0.315
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2019
Admission routes2
Has abstractyes

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