Bibliographic record
Abstract
Ageing is something that concerns me daily.I ponder the brown spots on my hands and the tinsel growing in my hair.I am attuned to the increasing aches and pains as I approach sixty.On a recent trip to Turin, the jet lag lasted much longer than usual upon my return home to Montreal.I wonder how much of this is due to the fact that I am getting older.In April, I had the pleasure of leading a writing workshop with students at the University of Turin.When I asked them to share the name of a person that they look up to, I was particularly struck by a quiet blond girl in the last row."My 94-year-old grandfather," she said proudly."He is the man I admire and respect the most."She went on to list the characteristics that make her grandfather an impressive role model.Like that student, I am very aware of the contribution that the elderly have made to my generation and to my children's.I was born in Italy and raised in Canada, and I am particularly sensitive to the condition of retired immigrants.They left their homeland to pursue opportunities in a foreign country, whose hosts were not always welcoming.Those who left post-World War II Italy were mostly uneducated labourers.They emigrated from small rural towns where everyone knew each other and settled in big urban centres, where they were practically invisible.They made a comfortable living as simple construction workers or piece workers in clothing factories.They saved their pennies to buy that first house and to send their children to university.Now in their seventies, eighties or nineties, they wait for their (grand)children to visit.Old age is a time of rest
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.158 | 0.070 |
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".