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Record W3126805742 · doi:10.1101/2021.02.02.429476

Life and work of researchers trapped in the COVID-19 pandemic vicious cycle

2021· preprint· en· W3126805742 on OpenAlexaff
S. Aryan Ghaffarizadeh, S. Arman Ghaffarizadeh, Amir H. Behbahani, Mohammad Mehdizadeh, Alison Olechowski

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProductivityWorkloadFeelingPandemicCoronavirus disease 2019 (COVID-19)Work (physics)Virtuous circle and vicious circlePsychologyInstitutionWork productivityPolitical sciencePublic relationsEconomic growthSocial psychologyMedicineManagementEconomicsEngineering

Abstract

fetched live from OpenAlex

A bstract COVID-19 has disrupted researchers’ work and posed challenges to their life routines. We have surveyed 740 researchers of which 66% experienced a decrease in productivity, 50% indicated increased workload, and 66% reported they have been feeling internal pressure to make progress. Those whose research required physical presence in a lab or the field experienced considerable disruption and productivity decrease. About 82% of this group will try to permanently reduce their work dependency on physical presence. Parents and those taking care of vulnerable dependents have been spending less time on research due to their role conflict. We further observed a gender gap in the overall disruption consequences; more female researchers have been experiencing a reduction in productivity and external pressure to make progress. The results of this study can help institution leaders and policymakers better understand the pandemic’s challenges for the research community and motivate appropriate measures to instill long-term solutions.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.095
GPT teacher head0.288
Teacher spread0.192 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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