Effects of Second-Official Language on Earning Premiums in Ontario, Canada: Case of Male and Female Differentials
Bibliographic record
Abstract
This paper is focusing on the effects of learning French as a second language, where English serves as the mother tongue amongst both male and female Ontarians. With other factors considered, the paper responds to the question; would it be beneficial in terms of earnings for both male and female residents of Ontario to either use only English, both English and French, or only French at work? Using census data from 2016, a log-linear model was fitted to a total sample 27,362 males and 22,752 females. Generally, there is significant difference (t=30.65>1.96; p=0.000<0.05) in wages between male and female workers. Also, the study concludes that there is only a significant difference in earnings if French is used alongside English at the workplace; however, both male and females need not to learn a second-official language while residing and working within Ontario as it does not benefit them in terms of their wages.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".