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Record W4213127868 · doi:10.26481/dis.20151117sg

Civil conflict and education

2015· dissertation· en· W4213127868 on OpenAlexaff
Silvia C. Gómez Soler

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicConflict, Peace, and Violence in Colombia
Canadian institutionsBrock University
Fundersnot available
KeywordsRemedial educationIncentiveOrder (exchange)Work (physics)Political scienceSpecial educationPublic relationsMathematics educationPsychologyPedagogyBusinessEngineeringEconomics

Abstract

fetched live from OpenAlex

The objective of this research is to better understand the relationship between civil conflict and the educational achievement of students who take part in the formal education system in Colombia through the use of econometric tools. The results suggest that it is possible that students who are facing difficulties associated to the internal armed conflict successfully continue with their studies, but they need a special follow up. Policy makers should work in the creation of new and better incentives to retain these students. More governmental resources should be spent in order to meet their special needs (i.e. psychological help, school supplies, special remedial tutorials) and supervise their academic progress at educational institutions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.041
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.006
Scholarly communication0.0070.004
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0410.002

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 head0.038
GPT teacher head0.393
Teacher spread0.355 · 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 source (direct Gemma or distilled Codex), 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
Published2015
Admission routes1
Has abstractyes

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