Information Frequency, Value, and Difficulty as Sources of Social Inequality: Competitive Imbalances on <i>Jeopardy!</i>
Bibliographic record
Abstract
Informational content and topics strategically curated by institutions underpin reward structures in knowledge economies. Using a historical database of 298,879 questions from popular American television trivia game show Jeopardy! , this article presents a case study revealing competitive inequalities rooted in a large, historical information corpus. Historically, women contestants comprise 45.5 percent of Jeopardy! contestants but only 32.5 percent of game winners. This raises questions about the fairness of the game and mechanisms underpinning the gender performance gap. Contestant gender and occupation are predictive of topical strengths and weaknesses. Information frequency, value, and difficulty are identified as knowledge properties that underpin competitive advantages and disadvantages. Questions with female answers on Jeopardy! are less frequent, valuable, and difficult than male and nongendered questions. This deprives women contestants of competitive advantages because contestants exhibit homophilous tendencies with gendered knowledge; women exhibit advantages with female questions, and men are advantaged by male questions. The Jeopardy! gender performance gap can be reduced—but not eliminated—by equalizing the frequency, value, and difficulty of gendered questions. As a microcosm of dominant cultural trends and powerful societal knowledge institutions, Jeopardy! is a broadly applicable case study revealing inequalities in ostensibly meritocratic knowledge evaluation systems. Results reveal specific means to identify and mitigate social inequalities in knowledge institutions and ostensibly meritocratic information-based competitions.
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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 | 0.005 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".