Bibliographic record
Abstract
World War II veteran Hank Dunfield is about to turn one hundred years old. The staff at Ponderosa Pine Lodge have recruited Sarah, a young nursing student, to keep Hank safe, comfortable, and in the building while they plan a grand centenarian celebration. There’s one problem: Hank doesn’t want to live that long. Seemingly opposites, Hank and Sarah kindle a deep friendship. Sarah fears the future with multiple sclerosis will be even more isolated, difficult, and painful than the isolated, difficult, and painful present. Hank, a tail gunner during the Second World War, opens his heart to share the deep knowledge of fear, luck, and flying into battle he learned over his. combat missions. Sarah and Hank find strength in each other as they face their deepest fears. Based on interviews with veterans in Alberta seniors’ homes and the skilled nurses who care for them, Flight Risk is the story of finding exactly who you need when you least expect it. An empathetic exploration of grief, friendship, and hope, this play asks what we lose when we ignore the knowledge of our elderly, challenges the way that we think about aging and death, and inspires a brighter, more compassionate future.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.187 | 0.073 |
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".