Reckless Gambles and Responsible Ventures: Racialized Prototypes of Risk-taking
Bibliographic record
Abstract
Risk-taking is sometimes admired and sometimes disparaged. In this research, we examined previously unexplored questions concerning how membership in social groups is related to expectations and perceptions of risk-taking. We propose that prototypes of risk-takers incorporate racial associations. We conducted five studies (NTotal = 1,603, predominantly White residents of the U.S.) examining whether prototypes of risk-takers—primarily reckless and responsible ones—activate racial stereotypes and discrimination. We first focused on whether participants perceive Black (vs. White) men as more likely to engage in risk-taking, broadly construed (Study 1). Next, we tested whether the trait attributions (Studies 2-3) and mental images constructed with the reverse correlation task (Study 3) of reckless risk-takers are more stereotypically Black (and less White) than responsible risk-takers. In Study 4, we employed an investment game to investigate participants’ willingness to trust targets we depicted using the racialized mental images of reckless and responsible risk-takers derived from Study 3. A final study examined whether thinking about reckless risk-takers evokes Black stereotypes broadly, including even positive stereotype content. Findings confirmed that reckless risk-takers were imagined as more phenotypically Black and as having more stereotypically Black traits (both positive and negative), compared to responsible risk-takers. Theoretical and practical implications for this novel stereotype content in the domain of risk are discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".