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.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".