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Record W3124349999 · doi:10.15273/allons-y.v1i1.10048

Cyclical Youth-Led Conflict as an Early Warning Indicator

2016· article· en· W3124349999 on OpenAlexvenueno aff
Michelle Legassicke

Bibliographic record

VenueAllons-y Journal of Children Peace and Security · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPeacebuildingPolitical scienceDemocracySpanish Civil WarCivil ConflictState (computer science)Development economicsInternational communityPolitical economyEconomic growthEconomicsPublic administration

Abstract

fetched live from OpenAlex

The dynamics of conflict are shifting. In the 2011 World Development Report, the World Bank stated that conflicts are now increasingly cyclical and intractable events; 90 percent of the civil wars that occurred in the 2000s were fought within countries that had experienced a domestic conflict in the past 30 years (World Bank, 2011). Countries are more likely to experience cycles of violence due to the persistence of weak state structures that cannot extend their reach into peripheral regions, leading to local instability (Kingston, 2004). Throughout the 1990s and early 2000s, the international community observed several states – in which external actors provided 50 percent of those states’ overall revenues – relapse into civil war (Call, 2012). Given the significant investment by the international community in peacebuilding projects in post-conflict states – whether democratic reforms, economic reforms, capacity building, or sustainable development – there needs to be a significant increase in research focused on civil war recurrence, as the trajectory of post-conflict states cannot be guaranteed without sustainable peace.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.014
GPT teacher head0.295
Teacher spread0.281 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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