Studying the Art of Growing Old with Metchnikoff, Hauser, Lowman, and Thompson: Advice About Aging, 1900-1960
Bibliographic record
Abstract
This work explores shifting attitudes about aging in the first half of the twentieth century by tracing the rise of four figures, and by examining discussions that surrounded their work on aging in the press.Bacteriologist Élie Metchnikoff, food scientist Gayelord Hauser, and advice columnists Josephine Lowman and Elizabeth Thompson were seen as authorities on their subjects and wrote during a period of significant change: increased longevity, the advent of retirement, and growing scientific interest in aging produced a plethora of press discussion that plunged into the "problem" of old age.Their 'prescriptions' captivated attention in both Canada and the United States, illustrating the growing search for management and improvement that dominated discussions of aging.It is argued that while aging became the specialization of experts who studied it objectively, popular messages relayed that there was an "art" to growing old, its success determined by preparation, attitude, and personal will.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".