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Record W2396920310 · doi:10.1017/s1049023x00021580

Civilian Protection and Humanitarian Assistance—Report of the 2009 Civilian Protection Working Group

2009· article· en· W2396920310 on OpenAlexaff
Geoff Loane, Jennifer Leaning, Sara Schomig, Alexander van Tulleken, Kelli N. O’Laughlin

Bibliographic record

VenuePrehospital and Disaster Medicine · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsCanadian Red Cross Society
Fundersnot available
KeywordsConfidentialityHumanitarian aidInternational humanitarian lawPublic relationsWork (physics)Service (business)Focus groupService delivery frameworkPolitical scienceHuman rightsPublic administrationBusinessLawEngineeringMarketing

Abstract

fetched live from OpenAlex

The concept of protecting civilians in armed conflict is enshrined in international humanitarian law and widely acknowledged in humanitarian norms. Making this concept operational in humanitarian service delivery is a challenge. Yet, there are many ways in which humanitarian workers could learn from local people about underlying tensions in their community and with these new insights adjust service delivery accordingly. The 2009 WG developed a qualitative verbal tool that could be used by humanitarian field staff to assist them in understanding issues of civilian protection from the local perspective. The attributes and uses of this tool are described in detail in this WG report. The fundamental aim of this tool is to enhance the capacity of humanitarian workers in their daily work to observe markers of activity and behavior and to inquire and listen from local people about what these markers might or might not mean. The WG emphasized the needs for confidentiality, focus on service delivery (not legal or advocacy work), and iterative routine integration of information gained into field team deliberations. The proposed tool is offered as a possible pilot step towards improving humanitarian understanding and response to local civilian protection needs.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.057
GPT teacher head0.337
Teacher spread0.280 · 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 designObservational
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

Citations1
Published2009
Admission routes1
Has abstractyes

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