Geriatric Politics and the American Presidential Election
Bibliographic record
Abstract
When Anita Wohlmann and Aagje Swinnen invited me to write a commentary on age and ageism in the upcoming 2024 American presidential election, I was entranced by the idea. As a Canadian, I have watched with fascination the dramas of American elections from the front row of our international border, beginning with the 1960 debates between Democrat John F. Kennedy and Republican Richard M. Nixon, the first on TV. Both were experienced politicians. Kennedy was a senator and Nixon had been Vice President under Dwight Eisenhower for eight years, thus expected to be debate winner and next President. But TV was not kind to him. Kennedy appeared fit, handsome, charismatic, camera friendly, and most importantly, much younger than Nixon, who was awkward, uncomfortable, hesitant, and sweaty (also recovering from a knee injury). Both were in their forties, Nixon only five years older than Kennedy (see Kraus, 1977).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".