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Record W3118732352 · doi:10.31686/ijier.vol9.iss1.2906

Academia During The Covid-19 Pandemic

2021· article· en· W3118732352 on OpenAlexaff
Gabrielle Abelskamp, J. Carlos Santamarina

Bibliographic record

VenueInternational Journal for Innovation Education and Research · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)PsychologyPerceptionEntertainmentHigher educationMedical educationPublic relationsPolitical scienceMedicine

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has had a profound effect on both education and research activities. A survey conducted within the geotechnical engineering and earth science academic communities between April 22 and 24 explored the variables that affect working efficiency and intellectual development during the pandemic period. We received 274 complete responses from faculty and graduate students in North America, Europe, South Korea, and Saudi Arabia. The four variables that correlate best with individuals’ perceived consequences of the pandemic are: setting daily goals, focus on academic tasks, time spent reading literature outside core research or on professional development, and commitment to exploring deeper scientific concepts. Overall, 28% of the respondents exhibit a positive outlook. For the other 72%, living with non-family members or with children, hindered access to needed materials, and excessive time spent with video entertainment exacerbated the perception of potential negative consequences of the pandemic. Observed percentages and trends are very similar across age, gender, living conditions and regardless of regional/national restrictions. Two complementary surveys addressed faculty choices for online education and student preferences. These results document the effective transition from in-person to online education using readily available technology, and highlight students’ preferences for in-person education followed by live online platforms; pre-recorded lectures emerge as the least preferable choice.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.370
GPT teacher head0.642
Teacher spread0.272 · 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 designNot applicable
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

Citations5
Published2021
Admission routes1
Has abstractyes

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