Bibliographic record
Abstract
Schemas are one of the most popular explanatory concepts in cultural sociology and are increasingly used in sociology more broadly. In this article, we ask the question: have schemas been good to think with? We answer this question by analyzing the ontological, epistemic, and methodological bases of schemas, including the conceptualizations, claims, assumptions, and methods that underpin the use of schemas in sociological inquiry. We show that sociologists have developed two distinct, contradictory, and often conflated perspectives on schemas, what we refer to as culturalist and cognitivist perspectives. We suggest that schemas have acquired a polysemic character in sociology, and that they have become a (more narrow and consequently more scientifically legitimate) proxy for Culture, and that these features have (paradoxically) facilitated the popularity of schemas within the discipline. Sociologists have recently begun to make the necessary advancements to turn schemas into a more useful explanatory concept, through both analytical improvements (by distinguishing schemas from both public culture and other forms of nondeclarative personal culture), and methodological innovations (for better deriving schemas from survey data, texts, and experiments). Yet, some challenges remain, and the analytical value of schemas remains promissory. We conclude by offering some guidelines for making more specific and measured claims about schemas in sociological research.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.040 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".