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Record W4399728161 · doi:10.32920/26046610.v1

Behind Closed Doors: Relationship Stressors and Domestic Violence During the COVID-19 Pandemic

2024· preprint· en· W4399728161 on OpenAlexaffabout
A. W. Rice

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsToronto Metropolitan UniversityProfessional Engineers Ontario
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)DoorsPandemicStressor2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologyMedicineVirologyEngineeringClinical psychologyOutbreak

Abstract

fetched live from OpenAlex

COVID-19 has introduced a plethora of relationship stressors, many leading to severe social issues such as an increase in domestic violence cases worldwide. This study uses two methods; first, an extended literature review to identify primary themes relating to relationship stressors and domestic violence during the pandemic. Then, three Canadian news media sources are analyzed to see how these themes were portrayed. News coverage from the start of the pandemic until June 2022 is analyzed to see which themes were the most commonly reported and the sentiment of the coverage. Gaining an understanding of how the Canadian news media represents the social issues identified in the literature review can reveal public attitudes toward the social issue and whether action is being taken to address these issues once they are reported in news media.

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)0.003
metaresearch head score (Gemma)0.010
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: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.796

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0050.003
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0010.001
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 head0.074
GPT teacher head0.393
Teacher spread0.319 · 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
Published2024
Admission routes2
Has abstractyes

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