Bibliographic record
Abstract
Purpose The purpose of this paper is to review research findings on the effects of a variety of disasters, including the events of 9/11, on the general public and members of organizations. Design/methodology/approach A literature search was undertaken. Three areas were targeted: impact on public attitudes and morale, on organizations and human resource management, and on organizational resilience. Findings Four conclusions were drawn. First, disasters such as 9/11 have immediate negative effects on emotions and behaviors. Second, with the passage of time these effects dissipate for most people. Third, organizations directly affected by 9/11 immediately stopped functioning. Fourth, fortunately many of these firms bounced back surprisingly quickly. Research limitations/implications Relatively little research has been conducted on the effects of 9/11, limiting our understanding of it. Practical implications The people in these resilient firms, those more directly affected by the terrorist attacks, made the difference. Human resource management practices and cultural values already in place were the key factors in bringing about a speedy recovery. These factors can also be developed in other organizations as a precaution. Originality/value This paper explores important yet still under‐researched areas of management and organizational responses to terrorism.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".