Does fast- or slow-running time make us happier: a tribute to Mihaly Csikszentmihalyi
Bibliographic record
Abstract
This article is written as a tribute to Mihaly Csikszentmihalyi (1934–2021), whose Experience Sampling Method and ideas about the relationship between time passage and optimal experience (flow) laid the ground for the analyses of the effects of perceived passage of time, time pressure, and social rhythms of life on the feelings of happiness. Popular literature abounds with statements that the accelerated rhythms of modern life are taxing us emotionally. This article examines findings that justify both our intuitive fears of the accelerated rhythms of modern life and findings about people’s apparent enjoyment of fast-running time. Data for the analyses in the article are taken from the 2003 Experience Sampling Survey of Time Use and Well-Being, conducted at the University of Waterloo in Canada, and from Canada’s 2010 General Social Survey.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".