Inequality within omnivorous knowledge: Distribution of Jeopardy! geography questions, 1984-2020
Bibliographic record
Abstract
Inequalities in the availability, quality, quantity and perceived importance of knowledge are important axes of stratification. This article uses the popular trivia game show Jeopardy! as a case study to reveal how knowledge institutions can reflect and reinforce information inequalities. Using a database of 40,153 geography-related questions from Jeopardy! episodes from 1984-2020, this article maps and analyzes international inequalities in the quality, quantity and complexity of questions associated with different countries. High-income countries from regions closer to the United States are relatively prominent on Jeopardy!. Results reveal both between and within-region inequality, as even within less-prominent regions, representation is disproportionately concentrated in a small number of countries. Although underrepresented, more obscure countries and regions tend to be used in more difficult and higher-stakes questions, as constructing easy questions about places with little information is challenging. Geographic knowledge was fairly static over time; characteristics of countries selected for Jeopardy! questions exhibited little change over the 38-year database, despite historical changes occurring throughout the world over the time period. Unique hierarchies in the importance and value of knowledge exist within the omnivorous, highbrow institution of Jeopardy!, which strategically includes knowledge from broader societal corpuses from around the world. Textual analyses can reveal strengths, blind-spots and biases in the knowledge bases of knowledge institutions.
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 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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".