Bibliographic record
Abstract
This thesis looks at the 'immigration status differentials' in time allocation to household work, value of household work, and determinants of participation rate in household work.In determining the time allocated to household work by immigration status, the data provided by General Social Survey (GSS) Circle 12 Individual Information Survey, on time spent on household work in Canada 1998 with about 6,944 respondents was used.Two methods of valuation of household unpaid work were used which were opportunity cost (before and after tax) and replacement cost.In deciding which method is best I recommend the use of replacement cost of valuing household work since GNP itself measures actual output produced.In the study, I anticipated that an average immigrant spends more time in household work than an average Canadian and that an average female generally allocates more time to household work than an average male based on socio-economic factors determining household unpaid work as seen in Gronau (1977) and Becker (1965).As expected, the results show that an average female allocates more time to household work than an average male and the difference is statistically significant.An average immigrant and Canadian allocate the same amount of time to household work.However, in maintenance and repairs, the results show that males' participation rate is higher than females' and an average Canadian participation rate in maintenance and repairs is higher than the immigrant with statistically significant difference.When other variables were introduced into the model using probit method of estimation, it was observed that there is no significant difference in participation rates between Canadians and immigrants.
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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 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".