Unraveling the What and How of Organizational Communication to Employees During COVID-19 Pandemic: Adopting an Attributional Lens
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".