Bibliographic record
Abstract
Abstract When sociologists examine the content of sociological knowledge, they typically engage in textual analysis. Conversely, this paper examines the relationship between theory figures and causal claims. Analyzing a random sample of articles from prominent sociology journals, we find several notable trends in how sociologists both describe and visualize causal relationships, as well as how these modes of representation interrelate. First, we find that the modal use of arrows in sociology are as expressions of causal relationship. Second, arrow-based figures are connected to both strong and weak causal claims, but that strong causal claims are disproportionately found in U.S. journals compared to European journals. Third, both causal figures and causal claims are usually central to the overarching goals of articles. Lastly, the strength of causal figures typically fits with the strength of the textual causal claims, suggesting that visualization promotes clearer thinking and writing about causal relationships. Overall, our findings suggest that arrow-based figures are a crucial cognitive and communicative resource in the expression of causal claims.
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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 | 0.038 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".