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Standardising and mapping open‐source information for crisis regions: the case of post‐conflict Iraq

2005· article· en· W1485603567 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDisasters · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversité de Sherbrooke
FundersCanada Research ChairsEuropean CommissionUniversité de Sherbrooke
KeywordsThematic mapHumanitarian aidDistribution (mathematics)Field (mathematics)Product (mathematics)Computer securityPolitical scienceGeographyBusinessCartographyComputer science

Abstract

fetched live from OpenAlex

Painting an accurate picture of the situation on the ground in countries in crisis is vital for the efficiency of humanitarian aid and reconstruction agencies. This study describes a method for standardising and mapping the plethora of open-source information. The test site for the study is post-conflict Iraq. Important information on aid distribution, reconstruction and security in Iraq can be derived from the reports of humanitarian aid agencies and the media, before being formatted, inserted into a database and mapped. The product is a visual, cartographic structure of otherwise random information, showing which organisations are working in the country, which thematic and geographic areas are being prioritized in the field, and which areas most frequently experience security events. This type of mapping not only highlights the overall working environment within different parts of the country, but it may also serve as a decision-making tool for donors and humanitarian aid agencies planning to deploy personnel.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.276

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 communication0.0000.001
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 head0.034
GPT teacher head0.324
Teacher spread0.291 · 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