Denis G Rancourt conference presentation slides, Towards understanding the government assault and persistent excess mortality (Canada focus)
Bibliographic record
Abstract
The conference organizers (United Conservatives riding, party in power in Alberta) asked me to give the all-cause mortality context for their conference about moving forward after the Covid assault. ABSTRACT: My team and I have gathered and analyzed a massive data collection of all-cause mortality, official mortality by cause, and almost 100 (77) socioeconomic factors, by province of Canada, and also (concentrating on Alberta) by provincial region and by municipality (and by county in the USA, and by sub-national regions in Europe). We apply advanced data mining methods (correlation clusters, hierarchical dendrograms, etc.) and try to answer what happened and why jurisdictions can be so different. We find different causes of EXCESS mortality during and after Covid for the different age groups (and also in different jurisdictions). Covid-measures harm is staggering, and we prove that deaths mostly cannot be due to pandemic COVID-19, if we accept their numbers. We quantify and show the nature of persistent (post-Covid) excess all-cause mortality, by jurisdiction and age. We also have place-of-death (hospital, home, etc.) data and show striking differences between provinces, and when and how they killed which age groups and different times during 2020-2024. Fentanyl poisoning plays a major role in younger ages in the Western provinces, etc. CONTENTS: *Canada (and by province, region)- Government narrative vs reality- Nature of the actual data- Quantitative methods- Associations with socioeconomic factors (major inter-prov. differences)- Shocking: heterogeneity, hotspots, YLL, persistent mortality *Alberta (and by region, municipality)- Demonstration that excess mortality not from COVID-19, by age group- Persistent excess mortality likely causes, by age group- Major differences and specificity compared to other provinces *Conclusion: Nature of the assault *World: (if time permits) (Proof: No spread or contagion) VIDEO: Towards understanding the government assault and persistent excess mortality (presentation in Calgary, 2025-03-03)https://denisrancourt.ca/videos.php?id=135https://www.youtube.com/watch?v=ZW442JMQxDA
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.081 | 0.012 |
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".