Bibliographic record
Abstract
This is a book about teamwork, and it took a team to write it.As coauthors newly relating to each other, we had to employ the methods of any successful safetyrelated endeavor: communication, team building, workload balancing, and threat and error management.None of that was easy.In book writing and editing, egos are always involved-in our case, the usual authorial ego quotient was multiplied by three.Marshaling the "team intelligence" necessary to produce what we hope is a useful and credible book became our overriding, collective goal.We were assisted by many far-fl ung helpers who advanced our thinking, connected us to the right resources, guided and advised us, and grabbed us by the collar when we sometimes strayed down the wrong path.We all benefi ted from the different personal and professional support networks that each of us brought to this work.The respective individual and joint acknowledgments of Suzanne, Bonnie, and Patrick are as follows:Along with Patrick and Bonnie-who were a joy to work with-Suzanne thanks several special people.Susan Bianchi Sands was her initial link to Robert Francis, whose explanation of the changes wrought by Crew Resource Management (CRM) led to the enlistment of Bonnie and Patrick as coauthors.Robert Francis, in turn, put us in touch with airline industry experts long involved with CRM. Jim Pitisci was an invaluable guide to the Airbus Training Center and provided much insight into understanding how pilots think.Jan Von Flatern also facilitated Suzanne's very educational visit to Airbus in Miami.Steve Predmore provided important explanations of how CRM is implemented in a major airline like JetBlue.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.366 | 0.276 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".