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Record W3026140281 · doi:10.1037/tra0000739

Mitigating social and economic sources of trauma: The need for universal basic income during the coronavirus pandemic.

2020· article· en· W3026140281 on OpenAlexaff
Matthew Johnson, Elliott Johnson, Laura Webber, Daniel Nettle

Bibliographic record

VenuePsychological Trauma Theory Research Practice and Policy · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsInstitute of Population and Public Health
Fundersnot available
KeywordsUnemploymentPandemicRecessionSocial distanceCoronavirus disease 2019 (COVID-19)PsycINFOShock (circulatory)Great DepressionDevelopment economicsPolitical science2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Basic incomeEconomicsEconomic growthMedicineVirologyMEDLINEKeynesian economicsLaw

Abstract

fetched live from OpenAlex

The COVID-19 pandemic is projected to cause an economic shock larger than the global financial crisis of 2007-2008 and a recession as great as anything seen since the Great Depression in 1930s. The social and economic consequences of lockdowns and social distancing measures, such as unemployment, broken relationships and homelessness, create potential for intergenerational trauma extending decades into the future. In this article, we argue that, in the absence of a vaccine, governments need to introduce universal basic income as a means of mitigating this trauma. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.009
Scholarly communication0.0040.007
Open science0.0010.007
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0060.001

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 head0.238
GPT teacher head0.499
Teacher spread0.261 · 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 designTheoretical or conceptual
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

Citations37
Published2020
Admission routes1
Has abstractyes

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