Understanding Regional Differences in Work Hours
Bibliographic record
Abstract
In recent years, differences in working hours between Canada and other countries have been the focus of a substantial body of research. Much less attention has been paid to regional differences in work hours, although differences in average annual work hours between some regions are of an order of magnitude that is similar to that of the Canada-U.S. difference. Using data from the 2004 Survey of Labour and Income Dynamics, this study examines how much of differences in working time between Ontario and five other regions of Canada can be explained by 'observable' differences, including differences in union status, industrial structure, job conditions and demographic characteristics. 'Observables' were relatively efficient in explaining differences in the shares of individuals working a short year and working a full-year, full-time schedule. However, they were not very helpful in explaining differences in long work hours, did not entirely explain the larger share of short-year workers in the Atlantic and in British Columbia, and did not explain the huge popularity of the 'low' full-year, full-time schedule in Quebec. These differences that remain unexplained suggest that 'unobservable' factors (those that are difficult to observe in household surveys) also contribute to regional differences in work hours. These include incentives related to wage inequality, possible tax incentives (or disincentives) built upon progressive taxation policies, differences in job conditions, in preferences and tastes, and in the shape of institutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".