Going All In: Unfavorable Sex Ratios Attenuate Choice Diversification.
Bibliographic record
Abstract
When faced with risky decisions, people typically choose to diversify their choices by allocating resources across a variety of options and thus avoid putting "all their eggs in one basket." The current research revealed that this tendency is reversed when people face an important cue to mating-related risk: skew in the operational sex ratio, or the ratio of men to women in the local environment. Counter to the typical strategy of choice diversification, findings from four studies demonstrated that the presence of romantically unfavorable sex ratios (those featuring more same-sex than opposite-sex individuals) led heterosexual people to diversify financial resources less and instead concentrate investment in high-risk/high-return options when making lottery, stock-pool, retirement-account, and research-funding decisions. These studies shed light on a key process by which people manage risks to mating success implied by unfavorable interpersonal environments. These choice patterns have important implications for mating behavior as well as other everyday forms of decision making.
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 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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; both teacher heads agree on what is shown here.
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".