Why Does Working from Home Vary Across Countries and People?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We use two surveys to assess why work from home (WFH) varies so much across countries and people.A measure of cultural individualism accounts for about one-third of the cross-country variation in WFH rates.Australia, Canada, the UK, and the US score highly on individualism and WFH rates, whereas Asian countries score low on both.Other factors such as cumulative lockdown stringency, population density, industry mix, and GDP per capita also matter, but they account for less of the variation.When looking across individual workers in the United States, we find that industry mix, population density and lockdown severity help account for current WFH rates, as does the partisan leaning of the county in which the worker resides.We conclude that multiple factors influence WFH rates, and technological feasibility is only one of them.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it