Encultured Biases: The Role of Products in Pathways to Inequality
Bibliographic record
Abstract
Recent sociological work shows that culture is an important causal variable in labor market outcomes. Does the same hold for product markets? To answer this question, we study a product market in which selection decisions occur absent face-to-face interaction between intermediaries and short-term contract workers. We find evidence of “product-based” cultural matching operating as a pathway to inequality. Relying on quantitative and qualitative observational data and semi-structured interviews with intermediaries in trade fiction publishing, we show intermediaries culturally match themselves to manuscripts as a normal feature of doing “good work.” We propose three organizational conditions under which “encultured biases” come to the fore in product selection, and a fourth resulting in inequalities along demographic lines and other markers of perceived cultural proximity and distance. We close with a discussion of other settings in which product-based cultural matching is likely to occur, call for the investigation of cultural matching beyond previously theorized conditions, and argue for the inclusion of cultural products in the broader movement toward reconsidering culture as a causal factor.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| 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".