Executive Decisions in Emergencies and Innovation in Supply Chain: A Suggested Model
Bibliographic record
Abstract
Covid-19 was very impactful on all USA States, with increased deaths and escalated trends every day, strong hit in various states on the top of them comes New York., New Jersey, and Michigan, during difficult times (April 20). That is why the authors have chosen the General Motors (G.M.) initiative to study and analyze. The crisis of medical supplies and ventilators shortage at the beginning of the pandemic deserved to be studied and synthesized as an inspiring experience and adds value to the discipline of social responsibility, crisis management, disaster management, strategic decision making and emergency management. The major objective of this study is designing a model that represents a Road Map for Emergency Management for CEOs and Executives. Content analysis was used to analyze the Covid-19 crisis and events' sequences in relation to Manitoba Health Disaster Management Model, and the fast decisions and actions made. This research will conclude with a top-level management implication: a designed road map that can be used for making future decisions in emergency management.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".