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Record W2909863994 · doi:10.14738/abr.611.5544

Economic Recession In Nigeria: An Important Risk Factor For Suicide

2018· article· en· W2909863994 on OpenAlexaboutno aff
Emmanuel U. Asogwa, John. O. Onyezere .

Bibliographic record

VenueArchives of Business Research · 2018
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNigeriansRecessionQuarter (Canadian coin)Economic riskGlobal recessionDevelopment economicsEconomic growthPolitical scienceEconomicsGeographyActuarial scienceKeynesian economicsLaw

Abstract

fetched live from OpenAlex

There was a general belief and assumption that Nigerians so much love life that none of its citizens can ever think of taking his own life. This might have been responsible for the World Values Survey Report in 2003, which ranked Nigerians among the happiest people in the world, and the 6th happiest people in Africa, and 95th in the world by the United Nations (UN) in spite of the glaring challenges confronting them. However, these good stories may have been faulted and the general belief and assumption dispersed by the worrisome high rates of suicide in Nigeria few months into economic recession, which started in the first quarter of 2016. This development is strange to Nigerians, and in view of this, there are grounds to consider an association between economic recession and increased suicide rates. This study, therefore, intends to establish that economic recession is an important risk factor for suicide, and recommend measures to prevent recession-induced suicide.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.085
GPT teacher head0.451
Teacher spread0.366 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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