MétaCan
Menu
Back to cohort
Record W3188570399 · doi:10.1080/1068316x.2021.1962867

COVID-19 and the courtroom: how social and cognitive psychological processes might affect trials during a pandemic

2021· article· en· W3188570399 on OpenAlexaff
Amy‐May Leach, Lyndsay R. Woolridge, Brian L. Cutler, Jeffrey S. Neuschatz, Baylee D. Jenkins

Bibliographic record

VenuePsychology Crime and Law · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsAffect (linguistics)PandemicSocial distanceExtant taxonContext (archaeology)DistancingPsychologyCoronavirus disease 2019 (COVID-19)Social psychologyCognition2019-20 coronavirus outbreakCriminologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Face (sociological concept)SociologyMedicineHistoryVirologyPsychiatrySocial science

Abstract

fetched live from OpenAlex

Around the world, almost every aspect of people's lives has been affected by the novel coronavirus (COVID-19). We focused on one context that has received relatively little attention to date: the courtroom. Guided by established psychological findings and theories, we explored how the emergence of COVID-19 and proposed protective measures against the virus (i.e. face masks, physical distancing) could affect legal decision-making at trial. For the majority of the phenomena that we considered, the extant literature predicted negative or mixed effects. Because it appears likely that extralegal factors related to the pandemic will affect outcomes, the fairness of proceedings must be called into question. Overall, this work suggests that the reopening of the courts might be premature. It also highlights the importance of leveraging established psychological findings to address questions arising from unpredictable events when direct research is not yet available.

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)0.004
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.274
GPT teacher head0.566
Teacher spread0.292 · 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.

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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venuePsychology Crime and LawSame topicMedical Malpractice and Liability IssuesFrench-language works237,207