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Record W3004260296 · doi:10.1109/mce.2019.2953792

Implementable Humanitarian Technology

2020· article· en· W3004260296 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueIEEE Consumer Electronics Magazine · 2020
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsIndigenousHumanitarian aidSri lankaDisciplinePopulationPolitical scienceProcess (computing)Engineering ethicsPublic relationsEconomic growthSociologyEngineeringComputer scienceLawEconomicsSocioeconomics

Abstract

fetched live from OpenAlex

In the 21st century, humanitarian crises have become very complex in nature and affect huge portions of the global population, not just the marginalized, discriminated, indigenous, and disaster/war hit communities, but also people in so called developed and relatively rich nations. A Humanitarian Engineer can be a student, academic or professional from multiple disciplinary backgrounds, who harness a concern for global humanitarian crises with their unique expertise and skill. Together they collaboratively research, analyze and engineer holistic, innovative solutions for this issue. The IEEE is recently focusing much needed widespread attention on humanitarian issues. One example is the annual Global Humanitarian Technology Conference, which is followed by many regional conferences such as the IHTC Canada and HTC Sri Lanka. Only three articles were accepted for this Special Section after a very thorough review process.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.026
GPT teacher head0.254
Teacher spread0.228 · 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