Why do some people thrive while others succumb to disease and stagnation?
Bibliographic record
Abstract
This chapter focuses on the role that personality plays in resilience across the lifespan. The concept of personality captures a combination of genetic, familial, social, and cultural elements, and thus is very useful in understanding differential patterns of development. In particular, this chapter highlights findings from our work with the Terman Life Cycle Study, the longest longitudinal study conducted to date, to demonstrate how core aspects of the individual may impact how he or she travels life's pathways and reacts to life's challenges. Our findings suggest that temperamental predispositions, internal stress, coping responses, social relationships, and health behaviors may all be relevant to whether an individual will thrive and stay healthy in the face of challenge or succumb to illness and disease. By identifying the mechanisms involved, we can better understand risk and intervene more effectively, with the goal of increasing resilience as people age. It is easy to observe striking individual differences in healthy aging. Consider these two cases drawn from our lifespan studies of longevity. Elmer was constantly on the go – involved in everything and friends with everyone. In the morning he raised funds for a benefit concert to support the children's hospital; in the afternoon he bowled with his buddies; in the evening he cared for his wife and enjoyed the company of his children and grandchildren.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".