Bibliographic record
Abstract
Background Gerontechnology is a growing field with a mission to shape and develop technology tools and training that can enhance the quality of life of aging adults.It offers the opportunity to researchers in a diverse set of disciplines to learn and grow together in the service of that opportunity.The goal of this paper is to describe my career trajectory from the perspective of a developing gerontechnologist.Research aim Describe patterns, influences, and influencers that have shaped my career.The review focuses on identifying significant events that led me from early work on understanding expert performance to the field of aging and cognition and on applied aging research making use of human factors methodology.Methods I review my formal and informal educational experiences as new research challenges arose in the context of societal changes.Results Opportunities provided by academia through formal education (McGill University, Carnegie Mellon University) and later through employment (Wilfrid Laurier University, University of Waterloo, Florida State University) coupled with happy accidents, as well as privileges such as sabbaticals, leaves, and new writing assignments, provided me with the chance to shift research areas multiple times, eventually settling into gerontechnology research.My research was also shaped by societal trends such as the microcomputer revolution and the shift in funding from individual to team research (CARNET, CREATE).Conclusion The range of research challenges that I have been fortunate to pursue was made possible by a complex web of influential teachers, students and colleagues, universities and institutes, professional associations, taxpayers in multiple countries, and most importantly, through family support.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".