Communicating Through a Pandemic: Insights on Crisis Communication from Steel Authority of India Limited, Rourkela Steel Plant
Bibliographic record
Abstract
Executive Summary The year 2020 will go down in the annals of history as the year of COVID-19, the year the whole world was left devastated. Even in 2021, countries are being ravaged by the virus, with no immediate end in sight. In fast-moving and uncertain situations, leaders face many questions for which they might not have any answers ( Argenti, 2020 ). Therefore, the question is how leaders can manage the communication environment with clarity, consistency and empathy during this period of extreme disruption ( Glinska, 2020 ). Crisis communication is generally defined as the accumulation and dissemination of information during crisis situations to alleviate the severity of the crisis. This study outlines and analyses the communication strategies and measures adopted by the management of one of the biggest manufacturing industries of the country, the Steel Authority of India Limited, Rourkela Steel Plant (RSP), during this ongoing COVID-19 crisis. A survey on the efficacy of the communication measures adopted by the management was conducted on 345 employees. The results showed a high level of support for all the implemented communication measures. Yet employees at the junior level articulated a need for better communication exchange with the top management. They perceived their voices as going unheard and sought additional communication channels connecting them to the highest authority. Considering the feedback received from the employees, additional communication measures were adopted. RSP’s Mass Contact Exercise was revived in an online format. A mobile app was developed containing all information and guidelines that an employee would need regarding precautions, prevention, testing and treatment of COVID-19. Quick and clear communication at every juncture let RSP tide through arguably the toughest pandemic period. Thus, it should always be ensured that appropriate communication channels are effectively implemented for vital information to reach every corner of the organization.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".