2023 Employment Outlook, Alzheimer's Awareness Month, and Heart Health.
Bibliographic record
Abstract
2022 was a very interesting year in the Canadian job market, record low unemployment and at the same time, labor shortages in many different sectors. So, what's the outlook for the job market in 2023? We dig into the results of a new survey on the topic, with Ahmed Borhot, Director of Workplace Solutions for \\"Manpower Recruiting\\".Age is going to catch up to all of us, nothing can stop it. But could there be hope to stop the progression of Alzheimer's Disease? We discuss the latest research with Dr. Joshua Armstrong with the \\"Alzheimer's Society of Canada\\".Finally, have you considered taking 'pre-emptive' action when it comes to maintaining a healthy heart? We learn about work being done right here in Calgary by the \\"Heart Fit Clinic\\", to give patients some insight into their own personal 'heart health'. We speak with clinic Founder, Diamond Fernandes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.206 | 0.039 |
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".