Examining working time arrangements using time use survey data
Bibliographic record
Abstract
This paper uses full-record time diary data from six studies conducted in four countries, Canada (1992), the Netherlands (1990 and1995), Norway (1980, 1990), and Sweden (1991), to analyse daily schedules of individual work time patterns.The work schedules are based on the combination of regular paid work, overtime work, second jobs, and any reported informal paid activity.We define work episodes as single occurrences of paid work activity separated by 60 or more minutes from any other paid work episodes.The reference work episode was the one that occurred during "core" hours, which were defined as 8:00 a.m.-6:00 p.m. for Canada and Netherlands, and as 7:00 a.m.-4:00 p.m. for Norway and Sweden.These "core" time definitions were based upon the frequencies of start and end-times of work episodes.Work episodes were calculated for all days, including weekend days.We identified seven theoretical work time arrangements possible during a day for each individual with reference to the core working hours.These classifications of work time arrangements extend from early morning to late night with three classifications being single arrangements and four being multiple and overlapping arrangements.Empirically these arrangements, in combination, generated 10 workday patterns for individuals.We found vast, though as yet statistically untested, differences in work time arrangements across countries and by sex.In general, men tended to be relatively evenly distributed over the work time arrangements defined for a typical day, while women tended to work a single episode during core hours only.2/3 of Canadian and Swedish men worked at least some time outside core hours, while nearly half of Dutch and Norwegian men only worked during the core period.Women in Sweden worked a wider range of hours than women in the other three countries.Where possible, this paper explores relations between other aspects of working arrangements and the timing of paid work episodes.We found a strong relationship between the scheduling and the duration of paid work.People who worked both pre-core to core and core to post-core episodes worked the longest hours, while people working post-core only or only working core to post-core episodes put in the fewest hours.iii Non-Technical SummaryTime diary research collects detailed accounts of the activities in which people engage over the course of a day.This paper uses time diary data from six studies conducted in four countries, Canada (1992), the Netherlands (1990and 1995), Norway (1980 and 1990), and Sweden (1991), to find out how people schedule their paid work (including overtime, moonlighting, informal or casual work) over the course of the day.In particular, we wanted to find out how often people work outside of normal business hours, and how people working non-standard hours fit work into the rest of their day.We also wanted to find out how many work episodes people undertook in a day.We defined a work episode as a period which starts as people begin to work having done other activities for at least an hour before working and ending when people again do other activities for at least an hour following doing paid work.While most people overall worked during standard business hours, work outside these hours was frequent in all four countries.While women in Sweden worked during a wider range of times of day than women in Canada, the Netherlands and Norway, women on the whole were more likely to only work during core business hours and to have only one episode of work in a day.Men, in contrast, were more evenly distributed across various working time arrangements.Nearly half of men in the Netherlands and Norway did some work outside regular business hours, and two-thirds of men in Canada and Sweden worked at least partly outside these hours.We identified ten main patterns of scheduling work throughout the day.These patterns are related to the total hours people worked.People who started work before standard hours, worked at least some standard hours and also worked some hours after the core period spent more of their day working than other groups of employed people.People who only worked after core hours or who started work during core hours and continued working after core hours worked the fewest hours.Table 2 -The Definitions of Work Time Patterns Start of Episode End of Episode Value Hypercode Preonly before core Before core 1 10000 Coreonly core core 1 1000 Postonly after core after core 1 100 Preendcore before core core 1 10 Corepost core after core 1 1 All present 11111
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".