Who remembers fake historical figures? Differentiating between passing knowledge and dispositional openness in cultural research
Bibliographic record
Abstract
What does it mean when individuals report having a wide variety of cultural knowledge and taste? Core contemporary theories propose different answers to this question, suggesting that cultural breadth is either rooted in the development of “passing knowledge” across multiple domains, or the expression of more general “dispositional openness” to a wide variety of culture. To adjudicate between these two perspectives I introduce the use of pseudo items into culture research, and integrate their usage with Bourdieu’s observations about “competence” and the “right to speak.” I find evidence for a dispositional openness account to claimed cultural knowledge, in addition to a known gender effect that is likely also rooted in dispositions. In closing I discuss how my findings may be suggestive of a new form of allodoxia for elites. I also discuss how pseudo items and other productively weird methodological tools can help refine our analyses of longstanding culture questions, while also generating new ones.
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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 | 0.019 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".