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Record W3037940633 · doi:10.1177/0021886320937026

Unraveling the What and How of Organizational Communication to Employees During COVID-19 Pandemic: Adopting an Attributional Lens

2020· article· en· W3037940633 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Applied Behavioral Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsBrock University
Fundersnot available
KeywordsPublic relationsPandemicContext (archaeology)GlobeAttributionPerspective (graphical)Coronavirus disease 2019 (COVID-19)BusinessPolitical sciencePsychologySocial psychologyGeography

Abstract

fetched live from OpenAlex

In 2020, the coronavirus disease (COVID-19) pandemic has resulted in a massive, unexpected, and sudden disruption to billions of employees around the world. Organizations and employees have been forced to transform their operational routines virtually overnight. This has resulted in unprecedented demands on managers to make decisions in very uncertain conditions. In times of crises such as those employees turn to organizational leaders for information, which heightens demands for effective communication of critical decisions (Van der Meer et al., 2017; Van Zoonen & Van der Meer, 2015). In general, in the “new normal” resulted from the COVID-19 pandemic many white-collar and professional employees are working from home. This presents a whole range of communication challenges. In response, organizations have adopted technology-driven solutions, where managers communicate time-critical information via multiple channels including but not limited to email, intranet, video conferencing, and other tools.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.139
GPT teacher head0.379
Teacher spread0.240 · 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