Management of Sports Facilities: Stress and Terrorism Since 9/11
Bibliographic record
Abstract
Since 9/11, the world has been on alert and it is just a matter of time before a sports facility is targeted. No empirical studies have examined the stress levels of employees in sports facilities. Tangential studies will show, stress symptoms, changes in behavior and life style continued long after 9/11 to the point that it became a habit and no longer an isolated event. However, there is still the question of a secure work environment for the employees of these sports facilities. The current level of security being implemented in sport facilities is no longer sufficient to ensure the safety of employees, participants and spectators. Recommendations have been chosen carefully and are budget dependent. The implementation of biometrics will potentially reduce the stress levels of the targeted work environments by making it a safer place. The increased level of stress in the work environment has been partially reduced by several stress management techniques that include: task redesign, flexible work schedules, participative management, increased employee autonomy, employee fitness programs and open lines of communication to voice on going concerns to insure the safety of fans, athletes and employees. A conclusion is there is still a major concern of a secure work environment for the employees of these sports facilities at this date. This is the challenge.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".