Bibliographic record
Abstract
…In April 1997, a young Virginia man, intoxicated and driving 90 mph in a 35 mph zone, runs his car off the road and hits a tree. A female passenger, a high school senior, is killed. The Virginia newspaper reports it as a “terrible accident.” An account executive drinks too much alcohol, and gets behind the wheel of a car. On his way home, he kills a pedestrian. In his defense, he says, “It was an accident.” A man drives twice the posted speed limit because he's late for work. He loses control of his vehicle, which jumps the curb and kills five pedestrians in the blink of an eye. His attorney notes that accidents are a part of “everyday life.” What do these events have in common? They were all described as “accidents.” How can this be? An “accident” is defined as something unlucky that happens without being planned or intended or something that occurs by chance. While we are all horrified, no one is surprised that these tragedies occurred. They cannot be called chance or random. Changing the way we think and the language we use affects the way we behave and also the way we respond to these events. The word “accident” promotes the concept that these events are beyond human control. The fact is, motor vehicle crashes and the resulting injuries and deaths are predictable events. That they are predictable also makes them preventable. That is the key. It means we have the power to alter the course of these events.… We have so many highway deaths each year that they equal having a 737 jet crash every day. You can bet the American public would not tolerate a jetliner going down daily, yet somehow highway crashes and their costs have come to be viewed as the expected and uncontrollable consequences of everyday life. We have to change that. Through individual and group actions, some so simple as just changing the language we use, significant gains against this enormous societal problem can be made. Do You Want to Continue Receiving EMN? If this issue contains a business reply card on the front cover, we still need to hear from you! Please return the card today to ensure uninterrupted delivery. The American Board of Emergency Medicine was recognized by the American Board of Medical Specialties and the American Medical Association as the 23rd medical specialty member board in September 1979, when it held the first examination to evaluate physicians who sought certification in emergency medicine. By the end of 2001, ABEM had certified 18,553 physicians. ABEM can be reached at 3000 Coolidge Rd., East Lansing, MI 48823–6319; (517)332–4800, http://abem.org, or at [email protected]. The American College of Osteopathic Emergency Physicians supports quality emergency medical care, and promotes the interests of osteopathic emergency physicians and education. Founded in 1975, ACOEP since has worked to establish and accredit training programs in emergency medicine. Acting as part of the AOA Council on Postdoctoral Training, the ACOEP currently accredits programs in emergency medicine, a fellowship in emergency medical services, and jointly accredits programs in emergency medicine/family practice, emergency medicine/pediatrics and emergency medicine/internal medicine. For more information, contact ACOEP at 142 East Ontario St., Suite 550, Chicago, IL 60611; (312)587–3709; (312)587–9951 (fax); http://acoep.org. The Emergency Nurses Association serves the emergency nursing profession through research, publications, professional development, and injury prevention. Since its creation in 1970, ENA has worked on defining standards of excellence for emergency nursing and promoted quality emergency care through continuing education activities. Contact the Emergency Nurses Association at 915 Lee St., Des Plaines, IL 60016–6569; (800)900–9659, http://ena.org.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".