Loss Aversion in Women’s Professional Golf: Results from the 2023 U.S. Open at Pebble Beach, California
Bibliographic record
Abstract
There have been three significant loss-aversion studies involving professional golfers; all focused exclusively on men. Each study found significant loss aversion in these real-world settings, in response to suggestions that loss aversion would disappear if the stakes were high enough and decision makers had a chance to learn from previous mistakes. Together, these three studies cast significant doubts on the validity of those caveats. But they also beg the question: How would results for professional women golfers compare? The current study focuses on loss aversion in women’s professional golf in what was the highest-staked competition in Ladies Professional Golf Association (LPGA) history: the 2023 U.S. Women’s Open at Pebble Beach, California. Results from that within-subject study, reported herein, show similar levels of loss aversion, and at a highly significant level. Implications are discussed.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".