Living Longer \nUsing today’s emerging technology to \naddress issues related to aging in \nCanada in the year 2032
Bibliographic record
Abstract
In April 2009, Canada’s Special Senate Committee on Aging released its final \nreport listing the issues affecting Older Adults (age 65 and over). This \ndemographic will account for one quarter of Canada’s population by the year \n2032. The report indicates the need for further research on aging and promotes \ntechnology as a tool to address these issues. Using Rogers’ theory of Diffusion of \nInnovations, Riley’s theory of Structural Lag, Davis’ Technology Acceptance \nModel and ethnographic research methods to observe trends in attitudinal shifts, \nthis research paper explores the adoption, affinity and application of existing and \nemerging technology to address the issues related to aging of Canadian Older \nAdults in the year 2032.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".