Pandemic risk management; protecting people while ensuring business continuity
Bibliographic record
Abstract
Abstract The COVID‐19 pandemic swept across the globe in the latter half of 2019, throughout 2020 and into 2021. In response, many organizations implemented work from home policies, while others stopped operations entirely in an effort to limit the spread throughout their workforce and supporting communities. This containment strategy was not universally viable; long‐term shutdowns impacted the economic viability of companies, and some industries were designated as an “essential service” and thus continued operations. These employers faced the proposition of balancing the needs of the business and the community with a continued responsibility to provide a safe workplace for employees. This paper demonstrates how the application of common risk management methodologies, such as bowtie analysis combined with an appropriate assurance and verification process (e.g., the lines of defense model), can help the risks associated with a resumption or continuation of in‐person operations in a pandemic to be better understood and ensure the measures in place to manage said risk are appropriate and effective.
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 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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".