Bibliographic record
Abstract
BACKGROUND: The aim of this study was to analyze the Canadian sex ration at birth (male divided by total births [M/T]), which approximates 0.515 and is influenced by many factors.METHODS: This study analyzed M/T related to the thirteen different regions and territories of Canada. In order to proceed with this analysis, anonymous data were downloaded from Statistics Canada, the Canadian Government agency that elaborates statistics (period 1971-2021).RESULTS: The results showed that there were 18,346,479 births (of which 9,416,634 males and 8,929,845 females, with M/T 0.5133, and 95% CI: 0.5130-0.5135). These data were significantly lower than the expected value (χ2=125.1, P<0.001). Within this framework, Quebec had the highest M/T (0.5139), and this was significantly higher, when compared with the aggregate of the rest. Indeed, M/T in Quebec was higher than almost all the other provinces and territories, Mexico, and the United States.CONCLUSIONS: Quebec is uniquely Francophile in the North American continent, in all aspects. It is uncertain why Quebec should have a higher M/T and it is possible that cultural factor/s may be somehow responsible, including sex-selective termination of female fetuses if there is a high percentage of ethnic groups with son preference. Further elucidation is only possible by access to individual maternal data and the performance of a multivariate analysis (logistic regression outcome male/female). Such comprehensive data is rarely available and then typically only to researchers within their own countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".