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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".