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Record W2154955376 · doi:10.5539/ass.v10n4p139

Review of Civilian Experience in Conflict Situation

2014· article· en· W2154955376 on OpenAlexvenueno aff
Aliyu Yero, Jamilah Othman, Ahmad Talib, Shamsuddin B Ahmad, Jeffrey Lawrence D Silva

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBattlefieldFaithPopulationPolitical scienceTerrorismRage (emotion)Conflict resolution researchPublic relationsLaw and economicsSociologyLawSocial psychologyPsychologyConflict resolutionHistoryEpistemology

Abstract

fetched live from OpenAlex

There is a growing concern among scholars on how the battlefield of conflict/wars have shifted to civilian populated areas causing huge pain and loss, previous studies have shown that civilians today have become entangled in a precarious situation where conflicts rage forcing them to either take charge of their fate or risk living in continuous fear of being used as scape-goats by either of the parties in conflict. This paper strife’s to bring to fore some of the challenges faced by civilians in conflict. Also, by reflecting on previous studies, room will be created for a prompt analysis of what civilians go through in the event of an impending conflict. Using existent literature, previous studies and reports from international organizations and actors in the field of conflict management, the paper highlight the nature, impact and experiences related to conflict and how these processes undermine the realization of basic human needs for survival among civilians. Borrowing from the Human Needs theory, the paper concludes that the realization of human potentials will not be attainable unless the need for security and safety is guaranteed. Failure to protect civilians in need of protection will ultimately put the faith of the people in their hands and thus promote the proliferation of light arms which has the potential for misuse and further endangering the civilian population.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.954
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.120
GPT teacher head0.498
Teacher spread0.378 · 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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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