In a recent provocative paper in this journal,
Bibliographic record
Abstract
hypothesis that the mean weekend return following changes in daylight saving time equals the mean weekend return throughout the rest of the year. The authors report that the average Friday-to-Monday return on daylight-saving weekends is 200–500 percent larger than the average negative return for the other weekends of the year. The � nding appears to hold not only in the United States and Canada where daylightsaving date patterns are similar, but also in the United Kingdom, whose patterns ostensibly differ from those in North America. The results also appear robust to alternative statistical methods based on time-varying conditional heteroscedasticity and/or bootstrapping. This paper provides further robustness tests of the results reported by Kamstra et al. I show that the difference between mean weekend returns for daylight-saving and non-daylightsaving weekends is signi � cant only for fall changes in daylight saving time and that the fall difference is driven by two outliers associated with international stock market crises. Two separate adjustments for the heteroscedasticity these outliers induce cause the signi � cance of the fall difference to vanish. The total sample (spring plus fall) difference remains marginally signi � cant for some indexes after heteroscedasticity adjustments with classical � xed-level hypothesis tests. However, Bayesian sample-size adjustments produce posterior odds ratios that consistently favor the null hypothesis of no daylight-saving anomaly over the alternative that the anomaly exists. I also fail to reject the hypothesis that daylight-saving and nondaylight-saving weekend returns have equal distributions. For these reasons, I question the
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".