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Record W3209505692

Effects of exposure to traumatic events on self-esteem among residents of the city of Goma : Case of Quartier Buhene

2021· article· en· W3209505692 on OpenAlexaboutno aff
Kasao Mutumay Dieudonne, Bahati Valentin

Bibliographic record

VenueInternational journal of innovation and applied studies · 2021
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsBarbarismHarassmentQuarter (Canadian coin)PopulationDemocracyPolitical instabilityPoliticsGeographySocioeconomicsCriminologyPsychologyPolitical scienceSociologyDemographySocial psychologyLaw
DOInot available

Abstract

fetched live from OpenAlex

For more than a decade, the east of the Democratic Republic of the Congo in general and more particularly North Kivu has been facing regionalist tensions and political divisions to which we add other phenomena such as: barbarism, insecurity, police and military harassment, killings and violence, wars, tribal conflicts causing permanent instability, major population movements as well as the volcanic eruption, killing several families and even to those close to them who are dear to them. Given the extent of this situation, we believe that the self-esteem of the inhabitants of the City of Goma, more particularly of the Buhene Quarter, is not at the normal level given that they are exposed daily to unfortunate and traumatic events. The purpose of this study is to examine the effects of exposure to traumatic events on the self-esteem of Gomatraciens in general and in particular among the inhabitants of Buhene. After analyzing the results, we found that the more the Congolese of North Kivu in general and in particular those of the city of Goma are exposed to traumatic events, the more their level of self-esteem becomes too low.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.252

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 communication0.0000.000
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 head0.030
GPT teacher head0.357
Teacher spread0.327 · 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
Published2021
Admission routes1
Has abstractyes

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